1. A method (1000) for configuring a plurality of biosensor electrodes (104) in a wearable device (106), the method (1000) comprising: receiving one or more bio-signals from a user (108) of the wearable device (106); determining a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the plurality of biosensor electrodes (104) includes at least, a reference electrode, a ground electrode, and one or more active electrodes; detecting one or more electrode performance parameters associated with each of the plurality of biosensor electrodes (104), wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode; and determining a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes, upon comparison of a magnitude of the detected one or more electrode performance parameters with a predefined threshold value.

2. The method (1000) as claimed in claim 1, wherein the one or more electrode performance parameters include one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, and a bias content.

3. The method (1000) as claimed in claim 1, comprising: receiving, over a predefined period, the bio-signal data from the second electrode configuration to determine a state of health of the user (108).

4. The method (1000) as claimed in claim 1, wherein while determining the second electrode configuration, the method (1000) comprising: determining a plurality of electrode configurations including the first electrode configuration by dynamically assigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes; comparing the magnitude of the one or more electrode performance parameters of each bio-sensor electrode corresponding to each of the plurality of electrode configuration with the predefined threshold value; and determining the second electrode configuration among the plurality of electrode configurations based on the comparison.

5. The method (1000) as claimed in claim 1, wherein the first electrode configuration and the second electrode configuration are determined by an analog multiplexing.

6. The method (1000) as claimed in claim 1, wherein the plurality of bio-sensor electrodes (104) is adapted to receive the one or more bio-signals related to at least one of a brain wave, a heart rate, a blood pressure of the user (108).

7. A wearable device (106) to analyze a state of health of a user, the wearable device (106) comprising: a plurality of bio-sensor electrodes (104) adapted to detect one or more bio-signals from the user (108); and a switching unit (302) in communication with the plurality of bio-sensor electrodes (104) and at least one processing unit (202), the at least one processing unit (202) is configured to: determine a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the plurality of biosensor electrodes (104) includes at least, a reference electrode, a ground electrode, and one or more active electrodes; detect one or more electrode performance parameters associated with each of the plurality of biosensor electrodes (104), wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode; and determine a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.

8. The wearable device (106) as claimed in claim 7, wherein the switching unit (302) is configured to receive the one or more bio-signals and assign each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the corresponding biosensor electrode upon determination of one of the first electrode configuration and the second electrode configuration.

9. The wearable device (106) as claimed in claim 7, wherein the at least one processing unit (202) may determine the second electrode configuration based on at least one of the one or more electrode performance parameters, accelerometer data, and a gyroscope data, wherein the one or more electrode performance parameters includes one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, and a bias content.

10. The wearable device (106) as claimed in claim 7, wherein the at least one processing unit (202) is configured to receive, over a predefined period, the bio-signal data from the second electrode configuration to determine a state of health of the user (108).

11. The wearable device (106) as claimed in claim 7, wherein to determine the second electrode configuration, the at least one processing unit (202) is configured to: determine a plurality of electrode configurations including the first electrode configuration by dynamically assigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes; compare the magnitude of the one or more electrode performance parameters of each bio-sensor electrode corresponding to each of the plurality of electrode configurations with the predefined threshold value; and determine the second electrode configuration among the plurality of electrode configurations based on the comparison.

12. The wearable device (106) as claimed in claim 7, wherein the first electrode configuration and the second electrode configuration are determined by an analog multiplexor.

13. The wearable device (106) as claimed in claim 7, wherein the plurality of bio-sensor electrodes (104) is adapted to receive the one or more bio-signals related to at least one of a brain wave, a heart rate, and a blood pressure of the user (108).

14. A system (102) for configuring a plurality of biosensor electrodes (104) to analyze a health of a user (108), the system (102) comprising: a switching unit (302) configured to receive one or more bio-signals and assign each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the corresponding biosensor electrode includes at least one of, a reference electrode, a ground electrode, and one or more active electrodes; and at least one processing unit (202) communicably connected to the switching unit (302), wherein the at least one processing unit (202) is configured to: determine a first electrode configuration of the plurality of biosensor electrodes (104) upon assignment of the one or more bio-signals to the corresponding biosensor electrode; detect one or more electrode performance parameters associated with each of the plurality of biosensor electrodes (104), wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode; and determine a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.

15. The system (102) as claimed in claim 14, wherein to determine the second electrode configuration, the at least one processing unit (202) is further configured to: determine a plurality of electrode configurations including the first electrode configuration by dynamically assigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes; compare the magnitude of the one or more electrode performance parameters of each bio-sensor electrode corresponding to each of the plurality of electrode configurations with the predefined threshold value; and determine the second electrode configuration among the plurality of electrode configurations based on the comparison.

16. The system (102) as claimed in claim 15, wherein the at least one processing unit (202) is configured to allocate a timeslot to detect the one or more electrode performance parameters of each of the bio-sensor electrode in each of the plurality of electrode configuration.

17. The system (102) as claimed in claim 14, wherein the at least one processing unit (202) is configured to detect variations in the bio-signal data obtained from the corresponding bio-sensor electrode.

18. The system (102) as claimed in claim 14, wherein the one or more electrode performance parameters includes one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, and a bias content.

19. The system (102) as claimed in claim 14, wherein the at least one processing unit (202) is configured to detect the one or more electrode performance parameters by measuring a strength and clarity of brainwaves including alpha waves.

20. The system (102) as claimed in claim 14, wherein the at least one processing unit (202) is configured to integrate an artificial intelligence (AI) module (308).

DESC:FIELD OF THE INVENTION

The present invention generally relates to medical devices and more particularly relates to a system and a method for configuring biosensor electrodes in a wearable device.
BACKGROUND

The field of medical science is experiencing rapid growth with significant research focused on understanding brain activity. However, the existing wearable devices with integrated health monitoring mechanisms currently may have several deficiencies. The forthcoming paragraphs detail a few instances of such deficiencies.
Limited Coverage and Spatial Bias in EEG Systems: The existing EEG systems often suffer from fixed electrode configurations, leading to spatial biases and limited brain region coverage. The issue is particularly acute in compact wearable EEG devices where electrodes allocated for reference and ground functions further limit the coverage area, hindering accurate source localization, and spatial mapping.
Temporal Resolution and Dynamic Brain Activity Monitoring: The existing EEG systems do not capture immediate or rapidly shifting brain activities, such as epileptiform bursts. Developing systems that can switch electrode configurations swiftly to capture various perspectives of these transient events is essential for in-depth study and advanced insights into neural dynamics. Continuous monitoring of specific brain activities, like burst seizures, requires quick configuration switches to obtain diverse viewpoints.
Signal Quality and Electrode Performance Optimization: The existing EEG systems lack sophisticated mechanisms for evaluating and selecting electrodes based on quantitative metrics like signal-to-noise ratio, impedance, and artifact susceptibility. Hence, the existing EEG systems have suboptimal configurations and compromised signal quality. The challenge is more pronounced in consumer wearables where replacing electrodes is impractical. Variations in skin-electrode contact, placement, and material quality over time further complicate the issue, necessitating dynamic electrode role assignment based on real-time performance assessments.
Compactness and User-Friendliness in Wearable EEG Devices: For consumer-grade wearable EEG devices designed for long-term use, it is crucial to minimize the number of electrodes while maximizing functionality and efficiency. This requires balancing compactness, user-friendliness, and cost-effectiveness with comprehensive brain activity monitoring. Innovative approaches are needed to derive maximum utility from a limited set of electrodes while maintaining wearability and user comfort.
Hardware Limitations and Power Efficiency Challenges: Wearable EEG devices often face limitations such as restricted battery life, bandwidth, and lower sampling rates compared to conventional systems. These constraints can result in the loss of high-frequency components and limit continuous monitoring duration. Traditional transistor switches used in electrode reconfiguration face speed and power consumption limitations, hindering efficient and rapid reassignment of electrode roles, necessitating power-efficient designs and optimized switching mechanisms.
Signal Integrity and Artifact Management in Dynamic Systems: Rapid electrode switching can introduce transient effects and signal artifacts during stabilization periods when electrodes transition to new roles. Electrode displacement, drift, and cross-talk between closely spaced electrodes in compact systems can lead to signal contamination and reduced spatial resolution.
Flexibility and Adaptability in Electrode Configuration: The existing wearable systems often feature electrodes with fixed functionalities and predetermined roles, limiting their versatility. There is a need for more flexible systems that can adapt to different bio signal acquisition needs (e.g., EEG, ECG, EMG) without requiring multiple dedicated electrodes.
Challenges Specific to Ear-EEG and Other Specialized Wearable Devices: The existing Ear-EEG devices face challenges due to the fixed orientation of the ear canal, which may not align optimally with neural source dipole orientations thereby leading to reduced signal amplitudes and increased interference. This suboptimal axial orientation can cause spatial smearing and distortion of scalp topography. The compact nature of specialized wearable devices further complicates electrode placement, signal acquisition, and maintaining signal quality while ensuring user comfort and practicality.
Thus, there is a need for a solution that overcomes the above-mentioned deficiencies.
SUMMARY

This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention.
The present disclosure discloses a method for configuring a plurality of biosensor electrodes in a wearable device. The method includes receiving one or more bio-signals from a user of the wearable device. The method further includes determining a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes. The plurality of biosensor electrodes includes at least, a reference electrode, a ground electrode, and one or more active electrodes. The method further includes detecting one or more electrode performance parameters associated with each of the plurality of biosensor electrodes. The one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode. The method further includes determining a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.
The present disclosure further discloses a wearable device to analyze a state of health of a user. The wearable device includes a plurality of bio-sensor electrodes and a switching unit. The plurality of bio-sensor electrodes adapted to detect one or more bio-signals from the user. The switching unit is in communication with the plurality of bio-sensor electrodes and at least one processing unit. The at least one processing unit is configured to determine a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes. The plurality of biosensor electrodes includes at least, a reference electrode, a ground electrode, and one or more active electrodes. The at least one processing unit is configured to detect one or more electrode performance parameters associated with each of the plurality of biosensor electrodes, wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode. The at least one processing unit is configured to determine a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.
The present disclosure further discloses a system for configuring a plurality of biosensor electrode to analyze a health of a user. The system comprising a switching unit, and at least one processing unit. The switching unit configured to receive one or more bio-signals and assign each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes, wherein the corresponding biosensor electrode includes at least one of, a reference electrode, a ground electrode, and one or more active electrodes. The at least one processing unit communicably connected to the switching unit. The at least one processing unit is configured to determine a first electrode configuration of the plurality of biosensor electrodes upon assignment of the one or more bio-signals to the corresponding biosensor electrode. The at least one processing unit is further configured to detect one or more electrode performance parameters associated with each of the plurality of biosensor electrodes, wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode. The at least one processing unit is also configured to determine a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.
To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates an environment depicting an implementation of a system for configuring a plurality of biosensor electrodes in a wearable device, according to an embodiment of the present invention;
Figure 2 illustrates a schematic block diagram of the system and components of the system for configuring the plurality of biosensor electrodes in the wearable device, according to an embodiment of the present invention;
Figure 3 illustrates a schematic diagram depicting a switching unit and a processing unit of the system, in accordance with an embodiment of the present disclosure;
Figure 4 illustrates a schematic diagram depicting an interaction of the switching unit with an analog-to-digital converter (ADC) of the system, in accordance with an embodiment of the present invention;
Figure 5 illustrates an exemplary depiction of a head of a user at different timestamps having different electrode configurations, in accordance with an embodiment of the present disclosure;
Figure 6 exemplarily illustrates a comparison in an acquisition of the bio-signal data from a conventional electrode system and the system, in accordance with an embodiment of the present disclosure;
Figure 7 illustrates a flow chart of a method for configuring the plurality of biosensor electrodes, in accordance with an embodiment of the present disclosure;
Figure 8 illustrates another embodiment of flow chart depicting a method for configuring the plurality of biosensor electrodes, in accordance with an embodiment of the present disclosure;
Figure 9 illustrates switching between the plurality of electrode configurations in an allocated dynamic timeslot, in accordance with an embodiment of the present disclosure;
Figure 10 illustrates a dynamic time-slot allocation and an advanced artifact rejection in the system, according to an embodiment of the present invention; and
Figure 11 illustrates an exemplary process flow comprising a method for configuring the plurality of biosensor electrodes, according to an embodiment of the present invention.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the inventive concepts, reference will now be made to example embodiments illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the inventive concepts is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the inventive concepts as illustrated therein being contemplated as would normally occur to one skilled in the art to which the inventive concepts relate.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.
Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the example embodiments is included in at least one example embodiment of the present disclosure. Thus, appearances of the phrase “in example embodiments”, “in another example embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same example embodiments.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of operations does not include only those operations but may include other operations not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
In the present disclosure, the reference electrode may be referred to as, but is not limited to, a reference node, reference point, standard electrode, control electrode, or baseline node. The ground electrode may also be referred to as, but is not limited to, a grounding electrode, chassis ground, shield electrode, electrical ground, or ground plane. The active electrode may be referred to as, but is not limited to, an active site electrode, measurement node, electrode of interest, primary sensor, or data collection electrode. These terms can be used interchangeably across biosensing systems, including but not limited to ECG, EMG, EOG and EEG, each involving specific sets of electrodes. This interchangeable terminology offers flexibility while maintaining the functionality and purpose of each electrode type, thereby enhancing the clarity and comprehensiveness of descriptions for biosensing systems that utilize ECG, EMG, EOG and EEG technologies.
The present disclosure relates to providing at least a mechanism of dynamically switching electrode roles and thus the reference regions as required at different time periods coupled with fast switching of electrode set up configurations. In contrast to the conventional method where one electrode serves as the reference and another as ground, the present disclosure provides a method which may allow for the utilization of all electrodes, which may be selectively designated as reference, ground, or active.
Figure 1 illustrates an environment 100 depicting an implementation of a system 102 for configuring a plurality of biosensor electrodes 104 in a wearable device 106, according to an embodiment of the present invention.
In an embodiment, referring to Figure 1, the system 102 may be implemented in, but not limited to, a user device or a cloud/remote server. In another embodiment, the system 102 may be implemented in the wearable device 106, which may include, but not limited to, a head gear or ear buds. The system 102 may be installed in the user device via an application installed in the user device and running on an operating system (OS) of the user device. The OS typically presents or displays the application through a graphical user interface (“GUI”) of the OS. In a non-limiting example, the user device may be a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, a smartwatch, a smart earphone, or any device capable of receiving information related to health of a user 108 from the plurality of biosensor electrodes 104 and display the information on the user device.
As depicted in Figure 1, a reference electrode, a ground electrode, and one or more active electrodes from the plurality of biosensor electrodes 104 may initially be chosen to form a first electrode configuration. However, the first electrode configuration may not necessarily be the most efficient. Therefore, an iterative electrode selection process may be carried out by one or more modules (shown in Figure 2) within the system 102, which may evaluate a performance of each electrode in a plurality of configurations including the first electrode configuration and determine a second electrode configuration.
The iterative process of continually refining the plurality of bio-sensors electrode configuration based on one or more electrode performance parameters, the system 102 may adapt to different applications and conditions thereby ensuring optimal signal quality. In one example, if the wearable device 106 may have four bio-sensor electrodes, then the system 102 may create a matrix to determine if a bio-sensor electrode among the four bio-sensor electrodes may be better suited as a reference electrode, a ground electrode or an active electrode by determining a signal-to-noise ratio (SNR) a contact impedance, a common mode rejection ratio, spatial orientation of electrodes, a bias content or a bio signal response. The contact impedance may refer to a resistance encountered at an interface between the bio-sensor electrode and the skin of the user 108. Lower contact impedance generally indicates better electrode-skin contact which improves signal quality by reducing noise and artifacts.
Figure 2 illustrates a schematic block diagram of the system 102 and components of the system 102 for configuring the plurality of biosensor electrodes 104 in the wearable device 106, according to an embodiment of the present invention.
The system 102 may include but is not limited to, at least one processing unit 202 or alternatively referred to as the processor 202, memory 204, modules 206, and data 208. The modules 206 and the memory 204 may be coupled to the processor 202.
The processor 202 may be a single processing unit or several units, all of which could include multiple computing units. The processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 202 is adapted to fetch and execute computer-readable instructions and data stored in the memory 204.
The memory 204 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 204 may alternatively be referred to as the database 208 in the present disclosure, within the scope of the invention.
The modules 206, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The modules 206 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions.
Further, the modules 206 can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processor 202 can comprise a computer, a processor, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor (e.g., processor 202) which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules 206 may be machine-readable instructions (software) which, when executed by the processor 202/processing unit, perform any of the described functionalities/methods, as discussed throughout the present disclosure.
In an embodiment, the modules 206 may include a receiving module 210, a detecting module 212, and a determining module 214. The receiving module 210, the detecting module 212, and the determining module 214 may be in communication with each other. The data 208 serves, amongst other things, as a repository for storing data processed, received, and generated by the modules 206. In an example, the modules 206 may be in communication with the remote server or the Cloud.
In an embodiment, the receiving module 210 may be configured to receive one or more bio-signals associated with the user 108 of the wearable device 106. The one or more bio-signals may be related to at least one of a brain wave, a heart rate, a blood pressure of the user 108. The determining module 214 may be configured to determine the first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes 104. The plurality of biosensor electrodes 104 may include at least, the reference electrode, the ground electrode, and the one or more active electrodes.
Further, the detecting module 212 may be configured to detect the one or more electrode performance parameters associated with each of the plurality of biosensor electrodes 104. The one or more electrode performance parameters may include one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, brainwave response, and a bias content. The one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode. Further, the determining module 214 may be configured to determine the second electrode configuration by reassigning each of the plurality of bio-sensor electrodes 104 as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters.
In an embodiment, once the second electrode configuration may be determined, the receiving module 210 may receive, over a predefined period, the bio-signal data from the second electrode configuration to determine a state of health of the user 108.
Figure 3 illustrates a schematic diagram 300 depicting a switching unit 302 and the processing unit 202 of the system 102, in accordance with an embodiment of the present disclosure. Figure 4 illustrates a schematic diagram 400 depicting an interaction of the switching unit 302 with the analog-to-digital converter (ADC) 304 of the system 102, in accordance with an embodiment of the present invention.
Referring to Figures 3 and 4 together, the system 102 may include the switching unit 302. The switching unit 302 may be configured to select the first electrode configuration of three or more bio-sensor electrodes among the plurality of bio-sensor electrodes 104 to designate as the reference electrode, the ground electrode, and at least one active electrode. The plurality of bio-sensor electrodes 104 may be disposed to monitor the one or more bio signals associated with the user 108. The switching unit 302 may be, but not limited to, an analog multiplexer or similar electronic device that may be used for the selection of the configuration from the plurality of electrode configurations.
In an embodiment, as depicted in Figure 3, the system 102 may include N electrodes interfaced with an M-channel analog-to-digital converter (ADC) 304 through the switching unit 302. Specifically, M multiplexers, each having an N:1 configuration, may be utilized to enable a connection of the N electrodes to the M ADC channels.
The system 102 may further include the at least one processing unit 202 to determine a magnitude of the one or more electrode performance parameters associated with each of the reference bio-sensor electrode and the at least one active bio-sensor electrode. The one or more electrode performance parameters associated with each of the plurality of biosensor electrodes, may be indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode.
In an embodiment, an Electrode Performance Index (EPI) may be determined by the processor 202 from the one or more electrode performance parameters. The EPI may include the one or more electrode performance parameters such as signal-to-noise ratio ( ?SNR?_i ), the impedance (? Z?_i ), the common mode rejection ratio ( ?CMRR?_i ), and the bias content ( K_i ). Each of the one or more electrode performance parameters may be assigned a specific coefficient to weight a contribution according to a relative importance in overall performance assessment. The EPI may be determined as shown in equation (1).
EPI i = W1 *SNRi - W2 *Zi + W3 *CMRRi + W4 * K……….(1)
Where:
SNRi= 10 log (Psignal/Pnoise)i
P_signal is power of an EEG signal at electrode i (in watts or microwatts)
P_noise is power of noise at electrode i (in watts or microwatts)
Zi is the impedance of electrode i (in ohms)
CMRRi is the common-mode rejection ratio at electrode i (in decibels)
K is a bias constant to account for additional factors not captured by SNR, impedance, or CMRR (unitless)
W1, W2, W3, and W4 are the coefficients (weights) for each term, which determine a relative importance in the overall performance index.
The equation (1) provides a comprehensive formula that allows for a systematic and quantifiable approach to evaluate electrode performance, enabling the iterative optimization process to identify the best configuration for specific applications.
Furthermore, the processor 202 may be configured to compare the electrode performance parameters of each of the plurality of bio-sensor electrodes 104 in the first electrode configuration based on a magnitude of bio signals data to a predefined threshold value. The processor 202 may further be configured to determine at least one other configuration (may alternatively be referred to as ‘the second electrode configuration’), different from the first electrode configuration of the plurality of bio-sensor electrodes 104 by dynamically switching the reference electrode, the ground electrode, and the at least one active electrode upon comparison.
In an embodiment, the processor 202 may include a control logic unit 306, typically a microcontroller or Field Programmable Gate Array (FPGA), to generate digital control signals needed to operate the switching unit 302. The control logic unit 306 may determine which electrode should be connected to the ADC 304 at any given time. The control logic unit 306 may send a digital signal to the multiplexer, selecting a specific input channel (electrode). The control logic unit 306 may connect the selected electrode to a common output line of the multiplexer. The analog switch associated with the selected input channel is then closed, allowing the biosignal from the electrode to pass through with high precision and low noise.
In an embodiment, an artificial intelligence (AI) module 308 may be incorporated within the system 102 that autonomously manages electrode configurations and bio signal acquisition using deep learning and reinforcement learning algorithms to optimize selections in real-time. The AI module 308 may have full access to hardware components like electrode switching matrices and analog-to-digital converters thus enabling rapid configuration changes for fine-grained control over data acquisition. The AI module 308 may be adapted to monitor the one or more electrode performance parameters such as signal-to-noise ratio (SNR), contact impedance, common-mode rejection ratio (CMRR), brain wave response, bias, and compute the Electrode Performance Index (EPI). Feedback mechanisms allow the AI module 308 to learn and adapt strategies based on signal quality as well as the spatial orientation, enhancing an ability to detect physiological states and events.
The system 102 may dynamically prioritizes relevant signals, using real-time analysis to switch electrode configurations in anticipation of events like seizures or sleep transitions. Techniques like adaptive filtering, independent component analysis (ICA), and wavelet transforms enhance signal quality. To manage energy, the AI module 308 may employ dynamic voltage and frequency scaling (DVFS). Advanced anomaly detection algorithms identify unusual patterns, adjusting sampling rates and configurations during critical events. The system 102, may implement as local Edge AI with federated learning, ensures high adaptability, sensitivity, and efficiency in biosignal acquisition, supporting long-term monitoring and privacy.
Referring to Figure 4, the system 102 includes the multiplexer 302 to select the first electrode configuration of four bio-sensor electrodes to designate as a reference electrode, a ground electrode, and active1 and active2 electrode based on a predefined criterion. The four bio-sensor electrodes are disposed to monitor bio signals referred to as signal1-signal4 associated with the user 108. The multiplexer 302 may be, but not limited to, an analog multiplexer or similar electronic device that may be used for the selection of the one or more configuration.
The system 102 further includes an analog frontend 402 that may be coupled with the processor 202 to detect the one or more electrode performance parameters associated with each of the reference bio-sensor electrode and the at least one active bio-sensor electrode based on the obtained bio signals data related to signal1-signal4 using the plurality of bio-sensor electrodes 104.
The processor 202 may further compare, via the plurality of bio-sensor electrodes in the first electrode configuration, the bio signals data to the predefined threshold value. In a non-limiting example, the first electrode configuration may be, when the first bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal “signal1” may be referred to as “active1” electrode, similarly, the second bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal “signal2” may be referred as “active2” electrode, third bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal “signal3” may be referred as reference electrode, similarly, fourth bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal signal4 may be referred as the ground electrode. The processor 202 further determines the second electrode configuration, different from the first electrode configuration of the plurality of bio-sensor electrodes 104 by dynamically switching the reference electrode, the ground electrode, and the at least one active electrode based on the comparison.
In a non-limiting example, the first electrode configuration may be when the first bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio-signal signal1 may be referred to as reference electrode, similarly, the second bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal signal2 may be referred as active2 electrode, third bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal signal3 may be referred as the ground electrode, similarly, fourth bio-sensor electrode amongst the four bio-sensor electrodes that corresponds to the bio signal signal4 may be referred as active1 electrode.
The analog frontend and/or the processor 202 may be configured to perform multiplexing, in a time domain, one or more time slots to the first electrode configuration and the at least one other configuration to acquire the bio signal data associated with the user. The processor 202 may, alternatively, select one of the first electrode configuration and second electrode configuration based on the comparison. The system 300 may further include the Analog to Digital Converter (ADC) 304 that may receive the processed bio signals from the processor 202, to convert the processed bio-signals into a digital form for analyzing the health of the user 108.
In an alternative embodiment, the system 102 may utilize detection of brain waves, including but not limited to alpha waves, as a metric for evaluating and selecting electrode configurations, as well as verifying the establishment of an effective electrode-brain connection. Alpha waves are neural oscillations within the frequency range of 8 Hz to 12 Hz, predominantly observed in occipital region of brain during a relaxed, awake state with eyes closed.
Furthermore, the system 102 may employ alpha wave detection as a means to validate a correct placement and positioning of biosensor electrodes over the targeted brain regions. By correlating the spatial distribution of alpha wave signatures with the known anatomical sources of such oscillations, the system 102 may confirm an accurate localization of the biosensor electrode. The strength and clarity of the detected alpha waves may serve as a quantitative metric for comparing and selecting electrode configurations. Configurations that yield stronger and more well-defined alpha wave patterns may be preferred, as it suggest superior electrode-brain connectivity and an ability to capture neural signals with higher fidelity.
By leveraging alpha wave detection, the system 102 may optimize electrode configurations for the EEG signal and other neural monitoring applications thereby ensuring appropriate electrode-brain coupling and enabling an acquisition of high-quality neural data from targeted brain regions.
Figure 5 illustrates an exemplary depiction of a head of the user 108 at different timestamps having different electrode configurations, in accordance with an embodiment of the present disclosure. Figure 5 depicts the different electrode configurations which demonstrate an ability of the system 102 to adapt and optimize the bio-sensor electrode placement over time to ensure optimal signal quality. The changes in electrode positioning at the different timestamps may reflect the iterative process of refining the electrode configuration based on the one or more electrode performance parameters such as the signal-to-noise ratio (SNR), common mode rejection ratio, bias content, bio signal response and the contact impedance. The dynamic adjustment of the plurality of bio-sensor electrodes 104 may aid in capturing high-quality bio-signals from different brain regions under varying conditions.
In one embodiment, the system 102 may be configured to evaluate and compare different electrode configurations for bio signal monitoring by utilizing a median electrode impedance of each configuration as a key metric, while also considering a global impedance. The electrode impedance refers to an opposition to flow of an ionic current between an electrode and skin or tissue surface to which the electrode may be applied. The median electrode impedance may be calculated across the plurality of biosensor electrodes 104 in a given configuration, providing an indicator of the overall electrode-skin contact quality and the potential for signal distortion or noise interference.
The system 102 may compare the median electrode impedance values among different electrode configurations. The electrode configuration exhibiting lower median impedance values may be characterized by better overall electrode-skin interfaces, leading to cleaner bio signal acquisition and reduced susceptibility to noise and artifacts. Conversely, configurations with higher median impedance values may be more prone to signal degradation and interference, potentially necessitating additional measures to improve electrode-skin coupling or the selection of alternative configurations.
By leveraging the median electrode impedance as a comparative metric, the system 102 enables informed decision-making in the selection and optimization of electrode configurations for various bio signal monitoring applications. Configurations with lower median impedance values may be prioritized or preferred, as they are more likely to provide consistent and reliable bio signal acquisition thus enhancing an overall quality and accuracy of the monitoring process.
Figure 6 exemplarily illustrates a comparison in an acquisition of the bio-signal data from a conventional electrode system and the system 102, in accordance with an embodiment of the present disclosure. Figure 6 illustrates 16 channels of EEG data, with boxed areas representing bio signal data streams collected. Graphical representations 6A and 6B of Figure 6 compares two different electrode configuration approaches for EEG systems. As depicted in graphical representation (6A) of Figure 6, a conventional EEG device having a fixed configuration that covers 4 channels. Figure (6A) showcases only a limited number of brain regions being monitored simultaneously due to static nature of electrode placement. This restriction may lead to incomplete data collection and potential gaps in capturing neural activity across different brain regions. The fixed configuration may lack flexibility; hence the conventional EEG device may be difficult to adjust in accordance with varying needs or conditions of the user 108.
As depicted in graphical representation (6B) of Figure 6, the system 102 rapidly cycles through different electrode configurations. The system 102 captures the data from multiple brain regions with minimal switching delay. By dynamically adjusting the electrode placement, the system 102 may collect a more comprehensive set of data thereby covering a broader area of the brain. The present approach addresses the challenges associated with fixed referencing and limited coverage inherent in the conventional EEG device.
Further, the rapid cycling of configurations ensures that the system 102 may continuously monitor various brain regions without significant interruptions or delays. This flexibility leads to more accurate and comprehensive data acquisition, enhancing the overall effectiveness of EEG monitoring process. The ability to switch configurations quickly allows the system 102 to adapt to different applications and conditions thus providing high-quality data for a wide range of use cases like epileptiform bursts tracking.
Figure 7 illustrates a flow chart of a method 700 for configuring the plurality of biosensor electrodes 104, in accordance with an embodiment of the present disclosure. Referring to Figure 7, the order in which the method steps are described below is not intended to be construed as a limitation, and any number of the described method steps can be combined in any appropriate order to execute the method or an alternative method. Additionally, individual steps may be deleted from the method without departing from the spirit and scope of the subject matter described herein.
In an embodiment, the method 700 begins at block 702. At block 704 the method 700 may include loading predefined electrode configurations. At block 706, following initialization, the method 700 includes assigning initial settings tailored for a plurality of use cases, such as, but not limited to, epilepsy, sleep, delirium, stress, or dementia. At block 708, once the electrode configurations may be loaded, the method 700 includes cycling through each of the plurality of use cases.
At block 710, the method 700 may include selecting a use case among the plurality of use cases and load the initial electrode configuration based on the selected use case. At block 712, the method 700 may include evaluating a performance of the bio sensor electrode by analysing bio signal data based on the one or more electrode parameters such as, but not limited to, the signal-to-noise ratio (SNR), the impedance, and the artifact susceptibility. At block 714, if the biosensor electrodes may perform adequately, the method 700 may include a usage of associated electrode configuration. At block 716, if the biosensor electrodes may be underperforming, the method 700 may enter into an iterative loop and keep switching the electrode configurations.
In this iterative loop, the method 700 may include identifying underperforming biosensor electrodes and eliminating the underperforming biosensor electrodes with better-performing alternatives. The method 700 may include re-evaluation of the new configuration with the better performing biosensor electrodes. The cycle of elimination, replacement, and evaluation of the biosensor electrodes may continue until an optimized configuration is achieved for the selected use case. This iterative refinement ensures that the system 102 continually improves the electrode configuration for optimal performance for the selected use case. Once an optimized configuration is determined, the method 700 further includes checking for next time slot or cycle for the system 102 to switch the new configuration and re-evaluate it. This ensures continual refinement and adaptation of the electrode configurations over time.
At blocks 718, 720, and 722, the user 108 may select the use case and an optimized electrode configuration may be applied accordingly. The method 700 may include cycling through the electrode configurations specific to the selected use case to maintain optimal performance. Therefore, the method 700 ensures that the system 102 may adapt dynamically to different applications and, also maintain high-quality bio signal acquisition and performance of the system 102.
Figure 8 illustrates another embodiment of flow chart depicting a method 800 for configuring the plurality of biosensor electrodes 104, in accordance with an embodiment of the present disclosure.
The method 800 begins at step 802, where the method 800 includes obtaining the bio signals data from each of the plurality of biosensor electrodes 104. At step 804, the method 800 may include determining the one or more electrode parameters based on the obtained bio signal data. The biosensor electrodes may be in communication with the switching unit 302. At step 806, the method 800 includes switching of the electrode configuration based on the determined one or more electrode parameters to optimize signal quality and coverage.
At step 816, the method 800 includes integrating the AI module 308 that may be central to a functionality of the system 102. The AI module 308 may manage both the electrode configuration switching (step 808) and electrode switching (step 810) based on the EPI of the associated electrode (step 812). The AI module 308 may leverage advanced deep learning architectures, including transformer-based models and reinforcement learning algorithms, to dynamically optimize electrode selections and configurations in real-time. By doing so, the system 102 may adapt to varying conditions and ensure high-quality data acquisition.
The AI module 308 may evaluate performance of the electrodes based on the EPI. The AI module may further be in communication with a Timeslot Allocation Module (step 814) which efficiently manages time allocation for the plurality of use cases thereby ensuring that each of the plurality of use cases receive necessary resources. Further, to maintain data integrity, the Timeslot Allocation Module may be in communication with an Artifact Rejection Module (step 818) which filters out unwanted noise and artifacts that may compromise the bio signal data. Additionally, the miscellaneous processes (step 820) handle other essential operations required for the functionality of the system 102 thereby ensuring smooth and comprehensive performance.
Further, insights may be generated by the Insight Generation Module (step 822) which processes the bio signal data to provide meaningful and actionable information. Further at step 824, the user 108 may have an ability to send custom insight requests thereby allowing the user 108 to tailor outputs of the system 102 to meet specific needs and requirements.
At step 826, the system 102 may interact seamlessly with a User Interface of the user device which may include multiple modes of interactions such as, but not limited to, text prompts, voice prompts, and a display unit for visual outputs. The user interface ensures that the user 108 may easily access and understand the insights generated by the system 102 thereby enhancing an overall user experience.
Figure 9 illustrates switching between the plurality of electrode configurations in an allocated dynamic timeslot, in accordance with an embodiment of the present disclosure.
The graphical representation 9A of Figure 9 showcases a default switching of the electrode configurations where configurations are cycled in each time window (T1, T2, etc.). Initially, four different configurations—Configuration 1, Configuration 2, Configuration 3, and Configuration 4—are assigned to time slots t1, t2, t3, and t4, respectively. Through four consecutive cycles of testing and evaluation, the system 102 iteratively refines the electrode configurations via electrode switching. By the end of these cycles, the system 102 may determine the most appropriate electrode configuration.
The graphical representation 9B of Figure 9 illustrates the process of reallocating time slots based on generated insights. If any concerning patterns or anomalies are detected, the system 102 may allocate additional time slots for more focused and detailed monitoring of the relevant signals, allowing for a deeper investigation into potential issues.
In an embodiment, the processor 202 may perform multiplexing, in the time domain, one or more time slots to the first electrode configuration and the second electrode configuration to acquire the bio signal data associated with the user 108. The multiplexing in time domain may be performed by the time division multiplexer which may be coupled with the processor 202. The processor 202 may, alternatively, perform selecting one of the first electrode configuration and the second electrode configuration based on the one or more electrode parameters. The system 102 may further receive, over a predefined period of time, the bio-signal data based on one of the multiplexing and selecting of configurations for analyzing health of the user 108.
In another embodiment, the processing unit 202 sequentially cycles through the predefined electrode configurations corresponding to each user-specified use case. The processing unit 202 analyzes evaluation metrics like signal quality, impedance, brain region coverage, the position of electrodes and hence the resulting priority, for each configuration. Based on these analyzed metrics, the system 102 automatically selects and sets the most appropriate electrode configuration tailored for that individual user, without requiring manual adjustments. This allows optimizing the configuration on a per-user basis at the start of bio signal monitoring.
In another embodiment, the processing unit 202 systematically cycles through predefined electrode configurations, each corresponding to a specific use case. For instance, within a given cycle, the system measures for epilepsy, delirium, dementia, sleep patterns, and sleep stages, utilizing distinct preset electrode configurations tailored to each condition. When epileptiform activity is detected, the system 102 prioritizes subsequent cycles to focus on epilepsy-related biomarkers. During these prioritized cycles, the system 102 sequentially deploys various configurations designed to identify the exact location and classification of seizures, while providing enhanced spatial coverage to capture detailed short-term activities, such as seizures.
In an embodiment, the electrode configurations for each use-case (epilepsy, dementia, sleep, delirium stress) are cycled through repeatedly with each cycle having a different configuration for each use-case. This means in every cycle, each use-case gets a variation in spatial coverage. The factors governing the selection of configuration for each use-case may be recorded electrode performance metrics or bio signal data quality.
In an embodiment, the second electrode configuration may be periodically changed after a certain time period ‘t’ to cycle through different possible electrode configurations. This may enable bio signal data acquisition using multiple regions as reference hence improving vantage point views for a particular event or episode.
In an embodiment, the system 102 may employ a hierarchical approach to bio signal monitoring, where it first allocates time slots for broad, general monitoring of multiple signals. If any concerning patterns or anomalies are detected, the system could then allocate additional time slots for more focused and detailed monitoring of the relevant signals, enabling a deeper investigation of potential issues.
In another embodiment, the system 102 may employ a fixed time slot approach for switching between different electrode configurations in neural monitoring. It divides the total monitoring period into a series of fixed-length time slots, each representing a predetermined interval for data acquisition from a particular electrode configuration. The allocation of time slots is based on the relative priority or significance of each configuration, with higher priority configurations receiving a greater number of slots without the need for dynamic adjustment of individual slot durations.
In one embodiment, time slots may be dynamically adjusted based on insights obtained from bio signal monitoring. For example, if the user's stress levels are determined to be significantly elevated, the time window allocated for stress level monitoring can be increased, while the time allocated for monitoring other parameters is correspondingly decreased, thereby providing focused attention to the stress levels during periods of prominence.
In another embodiment, custom time slot assignment may be employed. For instance, upon detection of epileptiform data, the system allocates additional time slots within a specific duration for epilepsy monitoring, while other monitoring configurations are deferred to the next minute. This embodiment ensures that epilepsy monitoring is given priority by assigning it a greater number of time slots.
An embodiment of the system 102 may integrate contextual information, such as the user's current activity, location, or environment, to intelligently adjust the time slot allocation. For example, if the user is engaged in physical exercise, the system may allocate more time slots to monitoring cardiovascular and respiratory signals, while reducing the time slots for other signals that may be less relevant during exercise.
In one embodiment, the total number of electrode configurations and iterations possible for head wearable bio signal measuring devices may be represented by a mathematical equation given by:
Configuration and Iteration Count = N C M * N - 2 C N - M
Where N is the total number of electrodes and M is the number of electrodes that are fixed (such as reference or ground electrodes). In another embodiment, the total number of electrode configurations and iterations possible for ear wearable bio signal measuring devices are represented by:
Configuration and Iteration Count = N - M C M * (L - 1) * N C M
Where N is the total number of electrodes, M is the number of electrodes fixed for that configuration and L is the number of concentric rings along which the electrodes (For Example in Ear EEG based Electrodes) may be distributed at equal distances from each other hence forming an array of electrodes.

In an embodiment, the system 102 may enable each electrode to be referenced to every other electrode, providing a multidimensional perspective of the bio signals instead of a single viewpoint that was achieved with a fixed reference. The present disclosure may also involve dynamically switching between different electrode configurations at a rapid pace, allowing the observation of the same event from various reference regions with minimal time gaps.
In an implementation, as compared to the conventional approach that may result in a loss of information in two electrodes, thus limiting the full utilization of electrodes as active systems, thereby resulting in reduced brain region coverage, and thus even for N number of Electrodes, all N may be used as active electrode, the present system 102 has many advantages such as the system 102 may provide the flexibility in automatically choosing the most optimum electrode configuration based on electrode signal characteristics (for example, but not limited to, low impedance, etc.) or programmatically based on the type of tests or according to specific requirements, while ensure optimal signal quality.
An embodiment of the system 102 employs rapid configuration changes to ensure comprehensive data acquisition across the brain; enhanced during critical events such as seizures and epileptiform discharges. The system 102 continuously evaluates electrode performance using metrics like signal-to-noise ratio, impedance, and spatial orientation, dynamically adjusting configurations to optimize signal quality and spatial coverage. This adaptive approach enables the system to capture subtle variations in brain activity, significantly enhancing the precision of seizure onset detection and localization.
The AI module 308 may perform dual functions within the system 102: performance-based iterative electrode configuration switching and comprehensive data analysis. Integrated accelerometer and gyroscope data may be utilized to differentiate various bio signals (EEG, ECG, EMG, EOG) from noise, particularly motion artifacts and suboptimal spatial orientation, ensuring high-fidelity data acquisition. This multi-modal approach enables the system 102 to distinguish between motion artifacts and genuine seizure-related movements, critical for implementing appropriate interventions in epilepsy management.
The present disclosure may also enhance the understanding of internal brain activity. The present disclosure may deepen the comprehension of neural processes and may potentially enable the development of personalized neurofeedback strategies. Additionally, such monitoring techniques also have valuable applications in sleep studies, meditation tracking, wellness monitoring, and monitoring of sleep apnea.
In an implementation, CMOS switch technology may be utilized in the switching matrix to dynamically reassign electrode roles (active, reference, ground, or null) within the electrode array. The low power consumption, due to the complementary nature of NMOS and PMOS transistors, combined with fast switching speeds, enables the integration of complex circuits with high transistor densities.
In another implementation, the switching matrix may also be achieved by a network of MESO (Magneto-electric spin-orbit) transistors technology or TFETs (Tunnel Field-Effect Transistors) or any other similar technology arranged to facilitate rapid role reassignment of electrodes. Control signals generated by the MCU (microcontroller unit) dictate the switching operations, enabling seamless transitions between different electrode configurations.
Figure 10 illustrates a dynamic time-slot allocation and an advanced artifact rejection in the system 102, according to an embodiment of the present invention.
In Figure 10, the switching of configurations within dynamically allocated time slots is depicted. The time slots are represented along a horizontal axis, with specific timestamps (t1 to t7) indicating when the electrode configuration switches occur. The vertical axis represents a signal amplitude. Each time slot may be allocated to capture bio signals from various electrode configurations thereby ensuring comprehensive data collection across different brain regions.
Further, during the configuration switches, the recorded signal segments are marked by noticeable dips or artifacts. These dips or artifacts occur due to transient effects of switching and are visually indicated by valleys in the signal of Figure 10. The system 102 may identify the artifacts and subsequently excludes the artifacts from consideration to maintain data integrity.
In an embodiment, by providing extended time slots that the artifacts may not overlap with useful signal segments. By extending a duration of each time slot, the system 102 may provide additional buffer time for the configurations to stabilize.
Figure 11 illustrates an exemplary process flow comprising a method 1100 for configuring the plurality of biosensor electrodes 104, according to an embodiment of the present invention. The method 1100 may be a computer-implemented method executed, for example, by the processor 202 and the modules 206 of the system 102. For the sake of brevity, constructional and operational features of the system 102 that are already explained in the description of Figures 1 to 10, are not explained in detail in the description of Figure 11.
At step 1102, the method 1100 may include receiving one or more bio-signals from a user of the wearable device.
At step 1104, the method 1100 may include determining a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes, wherein the plurality of biosensor electrodes includes at least, a reference electrode, a ground electrode, and one or more active electrodes.
At step 1106, the method 1100 may include detecting one or more electrode performance parameters associated with each of the plurality of biosensor electrodes, wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode.
At step 1108, the method 1100 may include determining a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters.
The system 102 incorporates an advanced artifact rejection technique to mitigate the impact of rapid electrode switching, which can introduce transient effects and signal artifacts. By dynamically assessing the required stabilization time for each electrode following a switch, the system 102 identifies and removes corresponding data segments from the bio signals. This process ensures that only high-quality, artifact-free portions of the bio signal are retained for subsequent analysis, thereby maintaining the integrity and reliability of the acquired data.
Central to this artifact rejection process is the Stabilization Time Detection step. This involves a sophisticated algorithm that conducts real-time analysis of bio signal variations to pinpoint periods of electrode instability. The detection mechanism is adaptable, taking into account individual differences and responding to dynamic changes in the signal. This tailored approach allows for precise identification of artifacts related to electrode stabilization, enabling their effective removal from the data stream.
In one embodiment, the bio-sensor electrodes are designed to capture various bio-signals, including brain waves, heart rate, and blood pressure. Electrodes with the lowest impedance may be selected for measuring and analyzing electroencephalogram (EEG) data. Another set of electrodes may be chosen for electrocardiogram (ECG) data measurement and analysis. Additionally, different electrodes may be used for measuring and analyzing body temperature and galvanic skin resistance.
The present disclosure provides various advantages:
The present disclosure renders a technique for reviewing each electrode and the one or more electrode parameters and accordingly designates a role for bio signal data acquisition.
The present disclosure may also enable personalized neuro-feedback and brain-computer interfaces (BCIs) tailored to individual characteristics. By utilizing dynamic electrode configuration and multiplexing techniques, the system 102 and method 1000 may further enable precise monitoring and modulation of brain activity of the user 108. The ability to selectively reference, switch between electrode configurations, and programmatically determining active, ground, and reference electrodes may allow for customized neurofeedback protocols and BCI applications. Such a personalized approach may empower the users to gain deeper insights into the brain dynamics and facilitate the development of adaptive and effective neurofeedback training regimens.
Additionally, the present disclosure may also facilitate the development of advanced BCIs capable of real-time decoding and interpretation of brain signals. This may further enable seamless interaction between the brain and external devices or systems, supporting a wide range of applications such as neurorehabilitation, cognitive enhancement, and assistive technologies.
The present disclosure may also introduce a comprehensive system 102 and the method 1000 for dynamic electrode configuration and multiplexing in bio signal monitoring. By combining selective referencing, impedance-based electrode selection, accelerated switching, and noise mitigation techniques, the system 102 and the method 1000 may significantly improve the signal acquisition and analysis. The disclosed techniques offer enhanced flexibility, improved signal quality, expanded electrode configuration possibilities, enhanced spatial and temporal coverage, improved power efficiency, and noise reduction, making it a valuable advancement in the field of bio signal monitoring.
While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. ,CLAIMS:WE CLAIM:
1. A method (1000) for configuring a plurality of biosensor electrodes (104) in a wearable device (106), the method (1000) comprising:
receiving one or more bio-signals from a user (108) of the wearable device (106);
determining a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the plurality of biosensor electrodes (104) includes at least, a reference electrode, a ground electrode, and one or more active electrodes;
detecting one or more electrode performance parameters associated with each of the plurality of biosensor electrodes (104), wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode; and
determining a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes, upon comparison of a magnitude of the detected one or more electrode performance parameters with a predefined threshold value.

2. The method (1000) as claimed in claim 1, wherein the one or more electrode performance parameters include one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, and a bias content.

3. The method (1000) as claimed in claim 1, comprising:
receiving, over a predefined period, the bio-signal data from the second electrode configuration to determine a state of health of the user (108).

4. The method (1000) as claimed in claim 1, wherein while determining the second electrode configuration, the method (1000) comprising:
determining a plurality of electrode configurations including the first electrode configuration by dynamically assigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes;
comparing the magnitude of the one or more electrode performance parameters of each bio-sensor electrode corresponding to each of the plurality of electrode configuration with the predefined threshold value; and
determining the second electrode configuration among the plurality of electrode configurations based on the comparison.

5. The method (1000) as claimed in claim 1, wherein the first electrode configuration and the second electrode configuration are determined by an analog multiplexing.

6. The method (1000) as claimed in claim 1, wherein the plurality of bio-sensor electrodes (104) is adapted to receive the one or more bio-signals related to at least one of a brain wave, a heart rate, a blood pressure of the user (108).

7. A wearable device (106) to analyze a state of health of a user, the wearable device (106) comprising:
a plurality of bio-sensor electrodes (104) adapted to detect one or more bio-signals from the user (108); and
a switching unit (302) in communication with the plurality of bio-sensor electrodes (104) and at least one processing unit (202), the at least one processing unit (202) is configured to:
determine a first electrode configuration based on assigning each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the plurality of biosensor electrodes (104) includes at least, a reference electrode, a ground electrode, and one or more active electrodes;
detect one or more electrode performance parameters associated with each of the plurality of biosensor electrodes (104), wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode; and
determine a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.

8. The wearable device (106) as claimed in claim 7, wherein the switching unit (302) is configured to receive the one or more bio-signals and assign each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the corresponding biosensor electrode upon determination of one of the first electrode configuration and the second electrode configuration.

9. The wearable device (106) as claimed in claim 7, wherein the at least one processing unit (202) may determine the second electrode configuration based on at least one of the one or more electrode performance parameters, accelerometer data, and a gyroscope data, wherein the one or more electrode performance parameters includes one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, and a bias content.

10. The wearable device (106) as claimed in claim 7, wherein the at least one processing unit (202) is configured to receive, over a predefined period, the bio-signal data from the second electrode configuration to determine a state of health of the user (108).

11. The wearable device (106) as claimed in claim 7, wherein to determine the second electrode configuration, the at least one processing unit (202) is configured to:
determine a plurality of electrode configurations including the first electrode configuration by dynamically assigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes;
compare the magnitude of the one or more electrode performance parameters of each bio-sensor electrode corresponding to each of the plurality of electrode configurations with the predefined threshold value; and
determine the second electrode configuration among the plurality of electrode configurations based on the comparison.

12. The wearable device (106) as claimed in claim 7, wherein the first electrode configuration and the second electrode configuration are determined by an analog multiplexor.

13. The wearable device (106) as claimed in claim 7, wherein the plurality of bio-sensor electrodes (104) is adapted to receive the one or more bio-signals related to at least one of a brain wave, a heart rate, and a blood pressure of the user (108).

14. A system (102) for configuring a plurality of biosensor electrodes (104) to analyze a health of a user (108), the system (102) comprising:
a switching unit (302) configured to receive one or more bio-signals and assign each of the one or more bio-signals to a corresponding biosensor electrode among the plurality of biosensor electrodes (104), wherein the corresponding biosensor electrode includes at least one of, a reference electrode, a ground electrode, and one or more active electrodes; and
at least one processing unit (202) communicably connected to the switching unit (302), wherein the at least one processing unit (202) is configured to:
determine a first electrode configuration of the plurality of biosensor electrodes (104) upon assignment of the one or more bio-signals to the corresponding biosensor electrode;
detect one or more electrode performance parameters associated with each of the plurality of biosensor electrodes (104), wherein the one or more electrode performance parameters are indicative of a quality of bio-signal data obtained from the corresponding bio-sensor electrode; and
determine a second electrode configuration by reassigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes, based on a magnitude of the detected one or more electrode performance parameters beyond a predefined threshold value.

15. The system (102) as claimed in claim 14, wherein to determine the second electrode configuration, the at least one processing unit (202) is further configured to:
determine a plurality of electrode configurations including the first electrode configuration by dynamically assigning each of the plurality of bio-sensor electrodes (104) as the reference electrode, the ground electrode, and the one or more active electrodes;
compare the magnitude of the one or more electrode performance parameters of each bio-sensor electrode corresponding to each of the plurality of electrode configurations with the predefined threshold value; and
determine the second electrode configuration among the plurality of electrode configurations based on the comparison.

16. The system (102) as claimed in claim 15, wherein the at least one processing unit (202) is configured to allocate a timeslot to detect the one or more electrode performance parameters of each of the bio-sensor electrode in each of the plurality of electrode configuration.

17. The system (102) as claimed in claim 14, wherein the at least one processing unit (202) is configured to detect variations in the bio-signal data obtained from the corresponding bio-sensor electrode.

18. The system (102) as claimed in claim 14, wherein the one or more electrode performance parameters includes one or more of, a contact impedance, a signal to noise ratio, a common mode rejection ratio, and a bias content.

19. The system (102) as claimed in claim 14, wherein the at least one processing unit (202) is configured to detect the one or more electrode performance parameters by measuring a strength and clarity of brainwaves including alpha waves.

20. The system (102) as claimed in claim 14, wherein the at least one processing unit (202) is configured to integrate an artificial intelligence (AI) module (308).