Description:FIELD OF THE INVENTION

[0001] The present invention relates to a neurological monitoring system that is capable of continuously assessing neurological conditions of a user, identifying potential neurological events in advance, and providing timely alerts to enhance user safety.

BACKGROUND OF THE INVENTION

[0002] Understanding the activity and responses of the human brain and nervous pathways plays a crucial role in modern healthcare and research. Continuous observation of neural patterns and physiological responses helps clinicians evaluate cognitive function, detect irregularities, and track recovery progress. The practices are vital in intensive care units, during surgical procedures, and in long-term management of conditions like epilepsy or traumatic injuries. By providing timely insights into neurological status, the approach supports informed medical decisions, enhances patient safety, and contributes to improved therapeutic outcomes in real-life clinical settings.

[0003] Conventional approaches rely largely on periodic clinical observation, manual assessments, and patient-reported symptoms to evaluate brain and nerve function. However, the practices depend on intermittent evaluations rather than continuous tracking, that limits the ability to detect subtle or sudden changes in real time. Interpretations vary among healthcare professionals, leading to inconsistencies in assessment. In many cases, delayed recognition of abnormalities affects timely intervention and recovery outcomes. Additionally, the methods require frequent hospital visits, increasing burden on patients and healthcare facilities while restricting comprehensive long-term insight.

[0004] WO2024006998A2 discloses a brain-computer interface ("BCI") systems that provide paralyzed individuals with more meaningful autonomy and independence. Including BCI systems used by an individual that requires less assistance from, or even in the absence of, a care giver, BCI systems that provide an objective and functional measurement of the effectiveness of a motor neuroprostheses in restoring motor outputs.

[0005] US20130072812A1 discloses a neural monitoring system for detecting an induced response of a muscle to a stimulus provided within an intracorporeal treatment area of a human subject includes a mechanical sensor configured to be placed in mechanical communication with the muscle and to generate a mechanomyography output signal corresponding to a sensed mechanical movement of the muscle, and a receiver in communication with the mechanical sensor. The receiver is configured to: receive the mechanomyography output signal from the mechanical sensor; compute a time derivative of an acceleration of the muscle from the mechanomyography output signal; compare the computed time derivative of acceleration to a jerk threshold; and indicate that the sensed mechanical movement of the muscle was induced by the provided intracorporeal stimulus if the computed time derivative of acceleration exceeds the jerk threshold.

[0006] Conventionally, many systems are disclosed in the prior art that provide a means for assessing neural function which relies on periodic observation, manual evaluation, and subjective clinical judgment. However, these existing systems limit continuous tracking and timely recognition of subtle changes. Moreover, real-time insight is minimal, making comprehensive and consistent evaluation of neurological conditions challenging and delayed.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that requires to be capable of persistently evaluating a person’s neurological status, analyzing functional changes in real time, and delivering tailored alerts and supportive responses. In addition, the developed system also needs to strengthen safety, enable early event detection, enhance clinical effectiveness, and promote sustained neurological well-being in daily and high-risk settings.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that continuously monitors neurological conditions of a user and detects abnormal neurological patterns indicative of potential adverse events at an early stage.

[0010] Another object of the present invention is to develop a system that provides timely warnings and appropriate preventive assistance to enhance user safety and reduce the risk of injury associated with sudden neurological events.

[0011] Yet, another object of the present invention is to develop a system that supports post-event evaluation and long-term neurological assessment while ensuring reliable operation, user comfort, and suitability for continuous everyday use.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a neurological monitoring system that is capable of continuously tracking neurological conditions of a user, detecting deviations indicative of potential neurological events, generating timely warnings, and supporting preventive assessment and post-event evaluation while ensuring user safety and uninterrupted daily use.

[0014] According to an aspect of the present invention, a neurological monitoring system, comprising a cap wearable over head of a user, one or more airbags with the cap, inflated upon detection of fall to safeguard against head injury, an IMU (inertial measurement unit) in the cap for detecting falling of the user, a plurality of dry electrodes, each of the dry electrodes with a spring loaded tip to enable optimal contact pressure against the scalp, a multi-modal bio signal acquisition arrangement with the cap to capture additional physiological parameters including cerebral blood oxygenation levels and hemodynamic responses, oxyhaemoglobin and deoxyhaemoglobin concentrations, sympathetic nervous system activation, abnormal head movements, convulsive activity, and postural changes associated with seizures or falls, a temporal artery with the cap to measure heart rate variability, showing significant alterations before neurological events, and a hybrid deep learning architecture with the cap for spatial pattern recognition and temporal sequence analysis of the sensed data.

[0015] According to another aspect of the present invention, the system further comprises a secondary classification model with the cap for identifying seizure types, enabling intervention protocols, a pre-seizure warning module with the cap for detecting pre-ictal patterns to initiate a three-stage alert cascade with escalating intervention recommendations, a confidence scoring protocol with the cap for preventing false alarms by requiring sustained pattern recognition across multiple signal modalities before triggering alert, a stroke risk assessment module with the cap to continuously determining risk of stroke through continuous monitoring of cerebral perfusion asymmetry and real-time analysis of blood flow patterns, an impedance monitoring circuits with the cap to continuously assess electrode-scalp contact quality across all channels, providing real-time feedback on signal integrity and alerting users to reposition the cap if impedance exceeds the threshold, and a post event analysis module with the cap to estimate the likely seizure onset zone using source localization applied to high-density EEG data.

[0016] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates an isometric view of a neurological monitoring system.

DETAILED DESCRIPTION OF THE INVENTION

[0018] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0019] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0020] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0021] The present invention relates to a neurological monitoring system that is capable of continuously observing neurological and physiological conditions of a user, identifying abnormal patterns associated with neurological events, providing timely alerts, and enabling preventive and post-event assessment, while ensuring user safety, comfort, and reliable operation during daily activities without intrusive clinical supervision.

[0022] Referring to Figure 1, an isometric view of a neurological monitoring system is illustrated, comprising a cap 101 including an outer layer 101a, an inner layer 101b, and a middle layer 101c, a plurality of airbags 102 connected with compressed gas cartridges 103 mounted with the cap 101, a plurality of shape-memory polymer bands 104 installed with the cap 101, a dual-wavelength LED emitter 105 installed in the cap 101, a plurality of vibration motors 106 embedded in the cap 101, a bone conduction speaker 107 provided with the cap 101, a microphone 108 installed on the cap 101 and a plurality of dry electrodes 109 installed within the cap 101.

[0023] The system disclosed herein comprises of a cap 101 wearable over head of a user. The cap 101 is ergonomically designed to comfortably conform to the user’s head while remaining suitable for prolonged wear during daily activities. The cap 101 is fabricated from lightweight, flexible, and breathable materials that promote airflow and moisture management. The structure provides durability, impact tolerance, and shape retention, ensuring a secure fit across varying head sizes without causing pressure or discomfort.

[0024] The cap 101 comprises an outer layer 101a providing electromagnetic shielding through a conductive mesh weave to block external interference while maintaining breathability through microporous channels. The outer layer 101a forms the protective exterior of the cap 101 and is constructed from a durable, flexible textile engineered to withstand daily wear. It provides mechanical resilience, resistance to environmental exposure, and shape stability. The material selection balances toughness with flexibility, ensuring the cap 101 maintains structural integrity while remaining lightweight, breathable, and suitable for continuous use in varied conditions.

[0025] The cap 101 further comprises an inner layer 101b of moisture-wicking construction. The inner layer 101b is formed from a soft, skin-friendly material designed for prolonged contact with the user’s scalp. It emphasizes comfort, moisture absorption, and ventilation to reduce heat buildup during extended wear. The material adapts gently to head contours, minimizing pressure points and irritation, while supporting stable positioning of the cap 101 during routine movements and activities.

[0026] The cap 101 includes a middle layer 101c including a flexible circuit board network with embedded electrode arrays providing EEG (electroencephalography) coverage. The middle layer 101c incorporates the flexible circuit board architecture that conforms to the curved geometry of the head without compromising electrical continuity. The middle layer’s flexibility allows consistent performance during head movement while maintaining durability against repeated mechanical stress and deformation.

[0027] The electrode array operates by establishing stable electrical contact with the scalp to detect neural bioelectrical activity generated by brain function. Each electrode maintains controlled contact pressure to reduce motion artifacts and impedance variability. Signals captured across multiple scalp regions are routed through the flexible circuitry to a microcontroller associated with the system, which coordinates signal synchronization, preliminary conditioning, and digitization before further processing. This arrangement enables reliable spatial coverage and consistent EEG signal acquisition during continuous monitoring.

[0028] The electrode array comprises a plurality of dry electrodes 109, each of the dry electrodes 109 including a spring loaded tip enabling optimal contact pressure against the scalp. The dry electrodes 109 operate by making direct electrical contact with the scalp without conductive gels. Each electrode 109 includes the spring-loaded conductive tip that automatically adjusts contact pressure to accommodate hair density and head movement, thereby maintaining stable impedance. The electrode 109 captures low-amplitude bioelectrical signals generated by neural activity and transmits them through conductive traces to the microcontroller. The microcontroller continuously monitors contact quality, filters noise, synchronizes multi-channel inputs, and forwards conditioned signals for further analysis.

[0029] The tip includes nano-textured silver-coated surfaces with micro-pillar structures to penetrate through hair to reach the scalp while maintaining low contact impedance. The electrode 109 tip works by using nano-textured silver-coated surfaces with micro-pillar structures to gently part hair and make direct contact with the scalp. The micro-pillars concentrate pressure at microscopic points, ensuring consistent electrical connectivity while minimizing discomfort. The silver coating enhances conductivity and reduces impedance. Signals collected at the tip are routed through the cap’s flexible circuitry to the microcontroller, which monitors contact quality and processes the signals for further neurological analysis.

[0030] Each electrode array incorporates active shielding means with driven-right-leg circuits that suppress common-mode noise. This circuitry actively counteracts interference, enhancing signal clarity. The electrodes 109 transmit the cleaned bioelectrical signals to the microcontroller, which continuously monitors and processes the data, ensuring high-fidelity neural recordings even during movement or in electrically noisy surroundings.

[0031] The cap 101 includes one or more shape-memory polymer bands 104 conforming to individual head geometries, ensuring consistent electrode-scalp contact across diverse patient populations. The shape-memory polymer bands 104 internally respond to body heat or slight mechanical stimuli, returning to a pre-programmed shape that conforms to the user’s head. This ensures consistent electrode-scalp contact across different head geometries. The microcontroller monitors sensor feedback to confirm proper fit and, if necessary, signals adjustments, maintaining stability of the cap 101 and optimizing signal acquisition during daily use.

[0032] An IMU (inertial measurement unit) is embedded in the cap 101 to detect falling of the user. The IMU combines accelerometers, gyroscopes, and magnetometers to detect linear acceleration, rotational movement, and orientation changes of the user’s head. Signals from the IMU are continuously transmitted to the microcontroller, which integrates the multi-axis data to determine movement patterns, falls, abnormal head motions, and postural changes. The microcontroller filters noise, applies calibration, and correlates motion data with other physiological signals to identify events such as falls or convulsive activity, triggering alerts or safety interventions.

[0033] Upon detection of fall of the user, the processing unit activates compressed gas cartridges 103 connected with a plurality of airbags 102 to inflate the airbags 102 to safeguard against head injury. The compressed gas cartridges 103 store pressurized inert gas used to rapidly inflate the airbags 102 when a fall is detected. Upon receiving a signal from the microcontroller confirming a fall, a valve means opens, releasing gas into the airbags 102. The microcontroller ensures that inflation occurs only under verified conditions to prevent accidental deployment. Gas flow is controlled to achieve rapid expansion while maintaining structural integrity, enabling immediate protective cushioning around the user’s head during a fall.

[0034] The airbags 102 are flexible enclosures integrated into the cap 101 that expand to cushion the head upon activation. The microcontroller triggers inflation after processing IMU and sensor data to confirm the fall or impact risk. Gas from the cartridges 103 enters the airbags 102 through controlled valves, causing rapid expansion. The airbags 102 absorb kinetic energy, reducing impact force to the skull and surrounding tissues. After deployment, the microcontroller monitors pressure decay and provide status feedback for maintenance or reset.

[0035] A multi-modal bio signal acquisition arrangement is associated with the system to capture additional physiological parameters including cerebral blood oxygenation levels and hemodynamic responses, oxyhaemoglobin and deoxyhaemoglobin concentrations, sympathetic nervous system activation, abnormal head movements, convulsive activity, and postural changes associated with seizures or falls and temporal artery positions to measure heart rate variability, showing significant alterations before neurological events.

[0036] The multi-modal bio signal acquisition arrangement comprises one or more Near-infrared spectroscopy (NIRS) sensors are embedded at frontal and temporal positions to non-invasively monitor cerebral oxygenation and hemodynamic changes, providing real-time insights into brain activity and potential neurological events. The NIRS sensors operate by emitting near-infrared light into the scalp and underlying brain tissue, where it is absorbed and scattered depending on oxyhaemoglobin and deoxyhaemoglobin concentrations. Photodetectors capture the returning light, and the intensity changes are converted into optical signals. These signals are transmitted to the microcontroller, which calculates cerebral blood oxygenation levels and hemodynamic responses in real time. By analyzing these variations, the microcontroller detect asymmetries, abnormal perfusion, or early physiological changes indicative of neurological events.

[0037] The multi-modal bio signal acquisition arrangement further includes Dual-wavelength LED emitters 105 paired with photodiode detectors enable continuous monitoring of oxyhaemoglobin and deoxyhaemoglobin concentrations. The dual-wavelength LED emitters 105 alternately transmit near-infrared light at two distinct wavelengths into the tissue, each wavelength being selectively absorbed by oxyhaemoglobin and deoxyhaemoglobin. The reflected light is captured by paired photodiode detectors and converted into electrical signals. These signals are routed to the microcontroller, which separates wavelength-specific data, compensates for ambient light interference, and computes relative oxygenation and blood volume changes for real-time physiological assessment.

[0038] Further, Electrodermal activity (EDA) sensors are integrated into temple-region electrodes to detect galvanic skin response changes that precede seizure events by monitoring sympathetic nervous system activation. The EDA sensors operate by applying a low, imperceptible electrical potential across the skin and measuring variations in skin conductance caused by sweat gland activity. Changes in conductance reflect sympathetic nervous system activation associated with stress or pre-seizure states. The sensed signals are transmitted to the microcontroller, which filters noise, normalizes baseline levels, and continuously tracks conductance trends. Significant deviations are correlated with other physiological parameters to identify early neurological event patterns.

[0039] The multi-modal bio signal acquisition arrangement further comprises 9-axis inertial measurement unit (IMU) combining accelerometer, gyroscope, and magnetometer is positioned at the cap's apex to detect abnormal head movements, convulsive activity, and postural changes associated with seizures or falls. Signals from each sensor are transmitted to the microcontroller, which synchronizes, calibrates, and filters the multi-axis data. By integrating these measurements over time, the microcontroller determines head movements, posture, falls, and convulsive activity. This processed motion data is then used to trigger safety interventions and correlate with other physiological signals.

[0040] Furthermore, Miniaturized photoplethysmography (PPG) sensors are embedded at temporal artery positions to measure heart rate variability, which shows significant alterations before neurological events. The PPG sensor operates by emitting light into the skin and detecting variations in reflected or transmitted light caused by pulsatile blood volume changes in underlying vessels. These optical fluctuations correspond to cardiac cycles. The detected signals are converted into electrical waveforms and sent to the microcontroller, which performs amplification, noise filtering, and pulse waveform extraction. The microcontroller computes parameters such as heart rate and heart rate variability, enabling correlation with neurological and autonomic changes.

[0041] A hybrid deep learning architecture is associated with the system for spatial pattern recognition and temporal sequence analysis of the sensed data. The hybrid deep learning architecture includes convolutional neural networks (CNNs) for spatial pattern recognition by extracting frequency-domain features from multi-channel EEG data, identifying characteristic spike-wave patterns, slow waves, and sharp transients associated with epileptiform activity. The CNNs process multi-channel neurological signal data by applying convolutional filters that automatically extract spatial and frequency-domain features, such as localized signal patterns and characteristic abnormalities. Pre-processed data supplied by the microcontroller is arranged into structured input matrices before inference. The CNN layers identify discriminative features by learning weighted kernels and pooling operations, reducing dimensionality while preserving salient patterns. The extracted feature maps are forwarded for higher-level temporal analysis and event classification within the hybrid architecture.

[0042] The hybrid deep learning architecture further includes long short-term memory (LSTM) networks for temporal sequence analysis, by analysing the temporal evolution of these patterns over sliding windows, detecting pre-ictal biomarkers that precede seizure onset. The LSTM networks process sequential neurological and physiological data to capture temporal dependencies. Inputs from the microcontroller, including preprocessed EEG, PPG, and motion signals, are fed into memory cells containing gates input, forget, and output that regulate the flow of information. The network retains relevant past patterns while discarding irrelevant data, enabling detection of pre-ictal biomarkers over time. The LSTM outputs temporal feature representations to subsequent layers or classification modules, supporting accurate prediction of impending neurological events.

[0043] A secondary classification model identifying seizure types (focal, generalized tonic-clonic, absence, myoclonic), enabling intervention protocols. The secondary classification model analyzes processed neurological data to differentiate between seizure types, such as focal, generalized tonic-clonic, absence, and myoclonic seizures. The microcontroller receives features from the primary processing modules and applies the model to assign seizure categories in real time. This classification allows the system to generate intervention protocols and alerts, optimizing user safety and ensuring appropriate responses for each seizure type.

[0044] A pre-seizure warning module is configured in the system to detect pre-ictal patterns are detected to initiate a three-stage alert cascade with escalating intervention recommendations. The three-stage alert includes first stage determining low risk, leading to gentle vibration motors 106 embedded in the cap 101, to provide tactile alerts to the wearer, suggesting environmental modifications.

[0045] The vibration motors 106 embedded in the cap 101 convert electrical signals from the microcontroller into mechanical oscillations. When the microcontroller detects a low-risk pre-ictal pattern, it sends a controlled voltage to the motor 106, causing an unbalanced rotor to spin and generate tactile vibrations. These vibrations provide gentle, perceptible alerts to the wearer. The microcontroller regulates intensity, duration, and timing, ensuring consistent feedback while minimizing power consumption and preventing discomfort during prolonged monitoring.

[0046] The second stage implying moderate risk, causing audio alerts through bone conduction speaker 107 provided with the cap 101 to notify the wearer. While wireless alerts are sent via a communication unit, to paired computing units of designated caregivers. The speakers 107 convert electrical audio signals from the microcontroller into sound waves that perceived by the user. The microcontroller encodes alert messages and modulates amplitude and frequency to produce clear, localized sound without affecting surrounding ambient noise. In moderate-risk events, the speakers 107 provide audible cues to warn the wearer. The microcontroller also synchronizes speaker 107 output with other alert modalities to ensure coordinated, timely notifications while conserving energy and avoiding distortion.

[0047] The communication unit enables wireless data transmission between the cap 101 and external devices such as caregiver smartphones or computing units. The microcontroller encodes alert signals, sensor data, and GPS coordinates into communication packets, which are transmitted via Bluetooth, Wi-Fi, or other wireless protocols. Incoming commands or acknowledgments are decoded by the microcontroller to adjust system behaviour. The communication unit ensures secure, low-latency communication, enabling real-time alerts, remote monitoring, and synchronization of intervention protocols without disrupting cap 101 operation.

[0048] The computing units receive processed sensor data, alerts, and classification results from the cap’s microcontroller via the communication unit. Internally, they perform higher-level computations, including trend analysis, historical data storage, and visualization of neurological events. The microcontroller coordinates data flow, while the computing units apply additional analytics, display alerts to caregivers, and maintain logs for post-event review. The computing unit also send configuration updates or intervention instructions back to the microcontroller to optimize system performance and user safety.

[0049] The third stage implying high risk, causing continuous alerts combined with automated emergency contact protocols to connect via pre-set contacts and transmit GPS (global positioning system) location coordinates detected via an integrated GPS unit. The GPS unit determines the user’s real-time location by receiving signals from multiple satellites orbiting the Earth. Each satellite transmits precise timing and positional data, which the GPS receiver in the cap 101 captures and converts into location coordinates. The microcontroller processes these signals, calculating latitude, longitude, and altitude, and continuously updates the user’s position. In the event of a high-risk neurological or fall scenario, the microcontroller transmits these coordinates via the communication unit to paired computing units or emergency contacts for rapid location tracking and assistance.

[0050] Furthermore, a confidence scoring protocol is associated with the system, preventing false alarms by requiring sustained pattern recognition across multiple signal modalities before triggering alert via the pre-seizure warning module. The confidence scoring protocol operates by analyzing multi-modal physiological and neurological data to verify the reliability of detected pre-ictal or abnormal events. The microcontroller receives signals from EEG, NIRS, IMU, PPG, and EDA sensors and computes confidence scores based on consistency across multiple modalities and signal quality. Only when scores exceed predefined thresholds does the microcontroller trigger alerts or intervention protocols. This means minimizes false alarms and ensures that only validated events initiate user notifications or safety responses.

[0051] A stroke risk assessment module continuously determining risk of stroke through continuous monitoring of cerebral perfusion asymmetry and real-time analysis of blood flow patterns. The stroke risk assessment module includes the NIRS sensors to continuously comparing oxygenation levels between left and right hemispheres, detecting asymmetries greater than 15% indicating potential ischemic events. The EEG frequency analysis identifies abnormal delta wave asymmetry and suppression patterns characteristic of acute stroke, implementing a Cerebral Ischemia Risk Index by combining multiple parameters, including oxygenation asymmetry, heart rate variability reduction, and sudden increases in electrodermal activity.

[0052] The stroke risk assessment module continuously monitors cerebral oxygenation, hemodynamic, and EEG patterns to detect potential ischemic events. The microcontroller receives input from NIRS sensors and EEG channels, analyzing asymmetries in oxygenation levels, abnormal delta wave patterns, and heart rate variability changes. It integrates these parameters to compute a dynamic Cerebral Ischemia Risk Index. When thresholds are exceeded, the microcontroller generates alerts, prompting preventive measures or caregiver notifications, allowing real-time stroke risk assessment and early intervention for the user.

[0053] An impedance monitoring circuits is associated within the cap 101 to continuously assess electrode-scalp contact quality across all channels, providing real-time feedback on signal integrity and alerting users to reposition the cap 101 if impedance exceeds the threshold. The impedance monitoring circuits continuously measure the electrical resistance between each electrode 109 and the scalp to ensure optimal signal quality. The microcontroller sends small test currents through the electrodes 109 and reads the resulting voltages, calculating impedance in real time. If impedance exceeds a set threshold due to poor contact, hair interference, or cap 101 displacement, the microcontroller generates feedback, prompting the user to adjust the cap 101. This ensures reliable EEG signal acquisition and prevents corrupted data during neurological monitoring.

[0054] A post event analysis module estimating the likely seizure onset zone using source localization applied to high-density EEG data. The microcontroller applies source localization protocols, such as dipole fitting or beamforming, using a simplified head model optimized for on-device computation. The results are transmitted to a companion app, where they are visualized as a 3D brain map highlighting probable onset regions like frontal, temporal, parietal, or occipital areas. The microcontroller also appends this information to clinical event logs, supporting neurologists in treatment planning and surgical evaluation.

[0055] A plurality of comprising temperature sensors is embedded throughout the cap 101 monitoring skin temperature, preventing overheating and automatically reducing power to affected components. The temperature sensors embedded throughout the cap 101 detect skin and local environmental temperature by measuring changes in electrical resistance, voltage, or current that correspond to thermal variations. Signals are continuously sent to the microcontroller, which interprets and digitizes the readings. The microcontroller monitors thresholds and adjust system operation, such as reducing power to sensitive components, to prevent overheating. This ensures user safety and protects electronics while providing continuous feedback for cap 101 performance and comfort.

[0056] If temperatures exceed a threshold temperature, the microcontroller activates humidity sensors detecting excessive moisture accumulation compromising electrode 109 function, triggering maintenance reminders, and IMU data is continuously analysed to detect removal of cap 101 or significant displacement, pausing monitoring and alerting functions. The humidity sensors detect moisture levels in the cap 101 environment by measuring changes in electrical properties (resistance, capacitance) caused by water vapor absorption in the sensing material. The microcontroller continuously reads these signals, calibrates them, and evaluates real-time humidity. If levels exceed set thresholds, the microcontroller generates maintenance alerts, pauses or adjusts monitoring to prevent electrode 109 malfunction, and ensures optimal user comfort. This allows proactive management of moisture accumulation and preserves reliable physiological signal acquisition.

[0057] A microphone 108 in combination with the speaker 107 installed on the cap 101, enabling hands-free communication during detected high-risk events. The microphone 108 converts acoustic sound waves into electrical signals using a diaphragm that vibrates in response to sound pressure variations. These vibrations generate corresponding electrical signals, which are transmitted to the microcontroller. The microcontroller amplifies, filters, and digitizes the audio for analysis, such as detecting voice alerts, environmental cues, or emergency sounds. It also synchronizes audio input with other physiological data. The processed signals trigger alerts, notifications, or data logging, supporting situational awareness and safety monitoring.

[0058] The present invention works best in the following manner, where the cap 101 as disclosed in the invention is worn on the head of the user during routine activities. Upon wearing, the cap 101 automatically conforms to the user’s head, ensuring stable positioning and comfort. The microcontroller initiates system startup by performing self-checks, verifying signal integrity, fit stability, and operational readiness before commencing continuous monitoring. During continuous use, the IMU embedded in the cap 101 detects head movements, posture, and potential falling of the user, while the electrode array comprising the plurality of dry electrodes 109 captures neurological signals. The multi-modal bio signal acquisition arrangement continuously acquires additional physiological parameters associated with neurological activity. The microcontroller synchronizes all sensed data and manages continuous monitoring.

[0059] In continuation, the hybrid deep learning architecture analyses the sensed data for spatial and temporal patterns, and the secondary classification model identifies seizure types when abnormal activity is detected. The pre-seizure warning module evaluates pre-ictal patterns and, upon validation through the confidence scoring protocol, initiates the three-stage alert cascade with escalating intervention recommendations. In parallel, the stroke risk assessment module continuously determines the risk of stroke by monitoring cerebral perfusion-related parameters. If the fall is detected and verified by the microcontroller, one or more airbags 102 mounted with the cap 101 are inflated using compressed gas cartridges 103 to safeguard against head injury. The impedance monitoring circuits continuously assess electrode-scalp contact quality and alert the user if repositioning is required. After the neurological event, the post event analysis module estimates the likely seizure onset zone using source localization applied to high-density EEG data, and the results are stored for clinical review. The system thereafter resumes continuous monitoring, ensuring ongoing neurological safety and assistance for the user.

[0060] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A neurological monitoring system, comprising:
i) a cap 101 wearable over head of a user;
ii) one or more airbags 102 mounted with the cap 101, inflated upon detection of fall to safeguard against head injury;
iii) an IMU (inertial measurement unit) embedded in the cap 101 detecting falling of the user;
iv) an electrode array comprising a plurality of dry electrodes 109, each of the dry electrodes 109 including a spring loaded tip enabling optimal contact pressure against the scalp;
v) a multi-modal bio signal acquisition arrangement capturing additional physiological parameters including cerebral blood oxygenation levels and hemodynamic responses, oxyhaemoglobin and deoxyhaemoglobin concentrations, sympathetic nervous system activation, abnormal head movements, convulsive activity, and postural changes associated with seizures or falls and temporal artery positions to measure heart rate variability, showing significant alterations before neurological events;
vi) a hybrid deep learning architecture for spatial pattern recognition and temporal sequence analysis of the sensed data;
vii) a secondary classification model identifying seizure types, enabling tailored intervention protocols;
viii) a pre-seizure warning module detecting pre-ictal patterns are detected to initiate a three-stage alert cascade with escalating intervention recommendations;
ix) a confidence scoring protocol preventing false alarms by requiring sustained pattern recognition across multiple signal modalities before triggering alert via the pre-seizure warning module;
x) a stroke risk assessment module continuously determining risk of stroke through continuous monitoring of cerebral perfusion asymmetry and real-time analysis of blood flow patterns;
xi) an impedance monitoring circuits continuously assess electrode-scalp contact quality across all channels, providing real-time feedback on signal integrity and alerting users to reposition the cap 101 if impedance exceeds the threshold; and
xii) a post event analysis module estimating the likely seizure onset zone using source localization applied to high-density EEG data.

2) The system as claimed in claim 1, wherein the cap 101 comprises an outer layer 101a, an inner layer 101b of moisture-wicking construction, a middle layer 101c including a flexible circuit board network with embedded electrode arrays providing EEG (electroencephalography) coverage and an outer layer 101a providing electromagnetic shielding through a conductive mesh weave to block external interference while maintaining breathability through microporous channels.

3) The system as claimed in claim 1, wherein the enclosure includes one or more shape-memory polymer bands 104 conforming to individual head geometries, ensuring consistent electrode-scalp contact across diverse patient populations.

4) The system as claimed in claim 1, further comprising one or more compressed gas cartridges 103 connected with the airbags 102 to inflate the airbags 102.

5) The system as claimed in claim 1, wherein the tip includes nano-textured silver-coated surfaces with micro-pillar structures to penetrate through hair to reach the scalp while maintaining low contact impedance.

6) The system as claimed in claim 1, wherein the multi-modal bio signal acquisition arrangement comprises one or more Near-infrared spectroscopy (NIRS) sensors are embedded at frontal and temporal positions, Dual-wavelength LED emitters 105 paired with photodiode detectors enable continuous monitoring of oxyhaemoglobin and deoxyhaemoglobin concentrations, Electrodermal activity (EDA) sensors are integrated into temple-region electrodes 109 to detect galvanic skin response changes that precede seizure events by monitoring sympathetic nervous system activation, 9-axis inertial measurement unit (IMU) combining accelerometer, gyroscope, and magnetometer is positioned at the cap's apex to detect abnormal head movements, convulsive activity, and postural changes associated with seizures or falls and Miniaturized photoplethysmography (PPG) sensors are embedded at temporal artery positions to measure heart rate variability, which shows significant alterations before neurological events.

7) The system as claimed in claim 1, wherein the hybrid deep learning architecture includes convolutional neural networks (CNNs) for spatial pattern recognition by extracting frequency-domain features from multi-channel EEG data, identifying characteristic spike-wave patterns, slow waves, and sharp transients associated with epileptiform activity and long short-term memory (LSTM) networks for temporal sequence analysis, by analysing the temporal evolution of these patterns over sliding windows, detecting pre-ictal biomarkers that precede seizure onset;.

8) The system as claimed in claim 1, wherein the pre-seizure warning module includes first stage determining low risk, leading to gentle vibration motors 106 embedded in the cap 101, to provide tactile alerts to the wearer, suggesting environmental modifications, second stage implying moderate risk, causing audio alerts through bone conduction speakers 107 provided with the cap 101 to notify the wearer, while wireless alerts are sent, via a communication unit, to paired computing units of designated caregivers, and third stage implying high risk, causing continuous alerts combined with automated emergency contact protocols to connect via pre-set contacts and transmit GPS (global positioning system) location coordinates detected via an integrated GPS unit.

9) The system as claimed in claim 1, wherein the stroke risk assessment module includes the NIRS sensors continuously comparing oxygenation levels between left and right hemispheres, detecting asymmetries greater than 15% indicating potential ischemic events, and EEG frequency analysis identifies abnormal delta wave asymmetry and suppression patterns characteristic of acute stroke, implementing a Cerebral Ischemia Risk Index by combining multiple parameters, including oxygenation asymmetry, heart rate variability reduction, and sudden increases in electrodermal activity.

10) The system as claimed in claim 1, further comprising temperature sensors embedded throughout the cap 101 monitoring skin temperature, preventing overheating and automatically reducing power to affected components if temperatures exceed a threshold temperature, humidity sensors detecting excessive moisture accumulation compromising electrode function, triggering maintenance reminders, and IMU data is continuously analyzed to detect removal of cap 101 or significant displacement, pausing monitoring and alerting functions.