TECHNICAL FIELD
Embodiments of the present invention relate generally to monitoring and analysis of electrophysiological signals and more specifically to a method and a system for reducing noise in electrophysiological signals.
BACKGROUND ART
Many electrophysiological signals from a human body and its constituent organs are monitored and measured to understand and diagnose various diseases and ailments affecting the human body. For instance, Electrocardiography is the measurement and graphing of electrical signals traveling through heart tissues and is used to estimate heart function. Similarly, Electroencephalography is the measurement and graphing of electrical signals traversing the brain and is used to estimate brain activity. These signals help us understand and estimate nature of various normal as well as disease processes within the human body.
However, the apparatus and methods we use to measure such signals introduce many types of noise and artefacts into the measured and recorded signal reducing its utility. Of the known types of noise introduced into the signal by such apparatus, “Baseline Wander” and “Power Line Interference” are two of the most important ones and effect even a stationary and still subject.
Baseline Wander is the fluctuation of the signal’s reference zero line due to (a) changing impedance between the electrodes used to measure the signal and the substrate such as skin that these electrodes are attached to or (b) other mechanical forces/movements affecting the electrodes and so forth. On the other hand, Powerline Interference is caused by residual Alternating Current wave energy filtering through to the circuitry and so forth of the measuring apparatus from its power supply or from nearby devices. In addition to the two mentioned above “Motion

Artefacts” produced by contracting muscles interfere with the signal whenever the subject moves or his muscles contract.
Overtime, several approaches and methods have been both proposed and implemented to reduce such noise and artefacts but they still leave much to be desired. Many a times these noise filtering methods introduce unintended features and artefacts into the signal confusing an observer.
Therefore, in light of the discussion above, there is need for a method and a system for reducing noise in electrophysiological signals, that does not suffer from above mentioned deficiencies.
OBJECT OF THE INVENTION
An aspect of the present invention provides a method for reducing noise in electrophysiological signals.
Another aspect of the present invention provides a system for reducing noise in electrophysiological signals.
SUMMARY OF THE INVENTION
Embodiments of the present invention aim to provide a method and a system for reducing noise in electrophysiological signals. A resultant signal so obtained in significantly free of baseline wander, power line interference, notches and unintended artefacts.
In accordance with an embodiment of the present invention, a method for reducing noise in electrophysiological signals, comprises steps of receiving the electrophysiological signals from a first device and normalizing a sampling frequency of the received electrophysiological signals, to obtain a first signal, filtering the first signal using a first filter, to obtain a filtered signal, smoothening the filtered signal by using a second filter, to obtain a smoothened signal, subtracting the smoothened signal

from the filtered signal to derive a second signal, down-sampling the second signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals, to obtain a down-sampled signal, flattening the down-sampled signal, to obtain a flattened signal, up-sampling and amplifying the flattened signal to match a sampling frequency of the smoothened signal, adding the up-sampled and amplified flattened signal to the smoothened signal to derive a third signal, down-sampling the third signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals and resampling the down-sampled third signal to a predetermined sampling frequency, to obtain a resultant signal having reduced noise.
In accordance with an embodiment of the present invention, the first filter is Butterworth filter.
In accordance with an embodiment of the present invention, the Butterworth filter is a 1-Hz high pass third order Butterworth filter.
In accordance with an embodiment of the present invention, the normalizing of the sampling frequency of the received electrophysiological signals comprises one up-sampling or down-sampling of the received electrophysiological signals.
In accordance with an embodiment of the present invention, the up-sampling or down-sampling of the received electrophysiological signals is performed to a sampling frequency in a range of 990 Hz to 1010 Hz.
In accordance with an embodiment of the present invention, the second filter is Savitzky-Golay filter with a sampling frequency dependent window length and first order polynomials.
In accordance with an embodiment of the present invention, a

system for reducing noise in electrophysiological signals, comprises a data interface module and a signal processing module. The data interface module is configured to receive the electrophysiological signals from a first device. The signal processing module is configured to normalize a sampling frequency of the received electrophysiological signals, to obtain a first signal, filter the first signal using a first filter, to obtain a filtered signal, smoothen the filtered signal by using a second filter, to obtain a smoothened signal, subtract the smoothened signal from the filtered signal to derive a second signal, down-sample the second signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals, to obtain a down-sampled signal, flatten the down-sampled signal, to obtain a flattened signal, up-sample and amplify the flattened signal to match a sampling frequency of the smoothened signal and correct for the attenuation of peaks in the smoothed signal respectively, add the up-sampled and amplified flattened signal to the smoothened signal to derive a third signal, down-sample the third signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals and resample the down-sampled third signal to a predetermined sampling frequency, to obtain a resultant signal having reduced noise.
In accordance with an embodiment of the present invention, the first filter is Butterworth filter.
In accordance with an embodiment of the present invention, the Butterworth filter is a 1-Hz high pass third order Butterworth filter.
In accordance with an embodiment of the present invention, for normalizing the sampling frequency of the received electrophysiological signals the signal processing module is further configured to up-sample or down-sample the received electrophysiological signals.
In accordance with an embodiment of the present invention, the

signal processing module is configured to up-sample or down-sample the received electrophysiological signals to a sampling frequency in a range of 990 Hz to 1010 Hz.
In accordance with an embodiment of the present invention, the second filter is Savitzky-Golay filter with a sampling frequency dependent window length and first order polynomials.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
Fig. 1 illustrates an exemplary environment to which various embodiments of the present invention may be implemented;
Fig. 2 illustrates a method for reducing noise in
electrophysiological signals, in accordance with an embodiment of the present invention;
Fig. 3A illustrates a graph representing normalized
electrophysiological signals, in accordance with an embodiment of the present invention;

Fig. 3B illustrates a graph representing electrophysiological signals that are free from Baseline Wander, in accordance with an embodiment of the present invention;
Fig. 3C illustrates a graph representing smoothened
electrophysiological signals, in accordance with an embodiment of the present invention;
Fig. 3D illustrates a graph representing a second signal, in accordance with an embodiment of the present invention;
Fig. 3E illustrates a graph representing a flattened second signal, in accordance with an embodiment of the present invention;
Fig. 3F illustrates a third signal, in accordance with an embodiment of the present invention;
Fig. 3G illustrates a graph representing a resultant signal having reduced noise, in accordance with an embodiment of the present invention; and
Fig. 4 illustrates a system for reducing noise in electrophysiological signals, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described, and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed

description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein

reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only, and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary, and are not intended to limit the scope of the invention.
The present invention aims to reduce noise in electrophysiological signals acquired from human body through procedures, but not limited to, like electrocardiography or electroencephalography. While there may be several kinds of noises in the electrophysiological signals, the most notorious ones are baseline wander and power line interference. The baseline wander may be significantly reduced using a Butterworth filter, while reduction of power line interference may require smoothening using another filter (such as Savitzky-Golay filter) and number of up-samplings and down-samplings, normalizing and flattening the electrophysiological signals. In that manner, the present invention has been described with help of an exemplary environment. However, a person skilled in the art would appreciate that the invention is not limited to the exemplary environment discussed below alone and can be implemented in various other physical environments, without departing from the scope of the invention.
Referring to the drawings, the invention will now be described in more detail. Figure 1 illustrates an exemplary environment (100) to which various embodiments of the present invention may be implemented. The

environment (100) includes a person (102) lying on a bed and undergoingan electrocardiogram (ECG or EKG) procedure. A first device (110) is configured to perform the ECG procedure using a plurality of electrodes (104). An exact number of the plurality of electrodes (104) may vary (for example, 3, 5 or 10) depending upon a specific application of the first device (110). Alternately, the first device (110) may also be configured to perform the electroencephalography procedure, etc.
The first device (110) is envisaged to include an output unit (112) that is configured to provide an output having the electrophysiological signals in raw and/or processed form to a person or machine responsible for making a diagnosis based on the output. In that manner, the output unit (112) may be, but not limited to, a display unit configured to display the output on a screen or a printer printing the output on a piece of paper etc. The first device (110) is connected to a central server (130) via a network (120). The network (120) may be implemented through a number of protocols such as 802.x, Bluetooth, ZigBee, Radio Frequency, GSM, CDMA. HSDPA and LTE etc., depending upon whether the network (120) is a Local Area Network (LAN) or a Wide Area Network (WAN). The central server (130) may embody a single device or a server stack etc. However, in several embodiments, the functionalities of the central server (130) may be inbuilt into the first device (110) itself. The central server (130) is envisaged to include a processor (such as a general purpose processor, a Field Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC) etc.) and a memory unit (such as EPROM, EEPROM and Flash Memory etc.).
A storage device (140) is connected to the central sever (130). The storage device (140) is configured to store all data generated/ received/ transmitted during the working of the present invention. The storage device (140) may represent, but is not limited to, an in-built storage, a distinct local storage device or a remote cloud based storage

etc. Also, connected to the network (120) is a computing device (125). The computing device (125) may be for example a mobile handheld device (such as a cellular phone, a PDA or a tablet PC etc.), a personal computer (such as a notebook or a desktop) or a dedicated device for studying electrophysiological signals. The present invention may now be described taking the exemplary environment (100) as a reference.
Figure 2 illustrates a method (200) for reducing noise in electrophysiological signals, in accordance with an embodiment of the present invention. The method begins at step 202 when the electrophysiological signals are received at the central server (130) from the first device (110). At first, the electrophysiological signals are pre-processed wherein the sampling frequency of the electrophysiological signals is normalized, at the central server (130), to obtain a first signal. In one embodiment of the invention, the normalizing of the sampling frequency of the received electrophysiological signals comprises one up-sampling or down-sampling of the received electrophysiological signals. In that manner, the up-sampling or down-sampling of the received electrophysiological signals may be performed to a sampling frequency in a range of 990 Hz to 1010 Hz. Referring to an embodiment (300) illustrated in Figure 3A, the normalized electrophysiological signals is represented which may include baseline line wander and power line interference.
At step 204, the first signal is filtered at the central server (130), using a first filter, to obtain a filtered signal. In one embodiment of the invention, the first filter is Butterworth filter. Furthermore, the Butterworth filter may be a 1-Hz high pass third order Butterworth filter. Normally, the order of the filter defines a logical number of filter elements applied sequentially to the electrophysiological signals. The logical number of filter elements may include, but not limited to, one or more capacitors and inductors. In other words, the order of the filter basically defines a roll off

rate. In general, the roll off rate refers to an action of a specific type of filter that is designed to roll off frequencies of the signal above or below a certain point. The roll off steepness may be stated in dB per Decade, with higher numbers indicating a steeper filter. For example, 40 dB/decade is steeper than 20 dB/decade. It is advantageous that the third order Butterworth filter is adapted to provide high roll off steepness without greatly increasing computational needs.
However, a small fixed amplitude correction may be necessary to align the electrophysiological signals to their true baseline depending on implementation of the first filter. It is envisaged that the first filter and configuration not only reduces baseline-wander but also does not introduce any unintended artefacts/features into the received and normalized electrophysiological signals, thereby increasing their utility. Further, a filtered signal is illustrated in Figure 3B. As shown in Figure 3B, the graph corresponding to the filtered signal is illustrated in voltage versus time. Herein, the preferred unit corresponding to voltage is millivolt (mV) and the preferred unit corresponding to time is seconds. The graph may also be represented in other units of voltage (for example, megavolt (MV)).
At step 206, the filtered signal is smoothened at the central server (130), by using a second filter, to obtain a smoothened signal. In one embodiment of the invention, the second filter is Savitzky-Golay filter with a sampling frequency dependent window length and first order polynomials. It is envisaged that window size may be selected such that the window size may be slightly greater than a wavelength of Alternating Current from Powerline Interference. Also, the first order polynomials are adapted to maximally smoothen out the Powerline Interference. The smoothened signal is illustrated in Figure 3C. An error is estimated after receiving the smoothened signal.

At step 208, the smoothened signal is subtracted from the filtered signal to derive a second signal, at the central sever (130). The second signal is envisaged to be a combination of error in the smoothened signal and that of the noise in the received electrophysiological signals as shown in Figure 3D.
At step 210, the second signal is down-sampled to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals, to obtain a down-sampled signal at the central server (130).
At step 212, the down-sampled signal is flattened, to obtain a flattened signal at the central server (130). In the context of the description here, “flattening” pertains to zeroing below a threshold relative to noise levels in the received electrophysiological signals. The flattened signal now is predominantly composed of error in the smoothened signal which is shown in Figure 3E.
At step 214, the flattened signal is up-sampled and amplified to match a sampling frequency of the smoothened signal and correct for the attenuation of peaks in the smoothed signal respectively.
At step 216, the up-sampled and amplified flattened signal is added to the smoothened signal to derive a third signal at the central server (130). The third signal is envisaged to include notches / artifacts as shown in Figure 3F. This allows for correction for attenuation in peaks in the smoothened signal.
At step 218, the third signal is down-sampled to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals to remove any notches / artifacts introduced or left behind from step 216.

At step 220, the down-sampled third signal is post processed such that the down-sampled third signal is resampled to a predetermined sampling frequency in order to obtain a resultant signal having reduced noise. The resultant signal having reduced noise is illustrated in Figure 3G. The resultant signal then may be transmitted from the central server (130) to the output unit (112) or computing device (125) for a medical professional to view and analyze.
In one embodiment, in case the normalized signal is free of baseline wander, the step 204 may be skipped/ avoided. In another embodiment, in case the filtered signal is free of Powerline Interference, the step 206 to the step 220 may be skipped.
It will be appreciated by a skilled addressee that the steps corresponding to the method may be interchanged depending upon the received electrophysiological signals without departing from the scope of the present invention. For example, the step 202 may be performed after removal of the power line interference from the electrophysiological signals.
Figure 4 illustrates a system (400) for reducing noise in electrophysiological signals, in accordance with an embodiment of the present invention. The system (400) includes a data interface module (410) and a signal processing module (420). For the environment (100), the data interface module (410) and the signal processing module (420) are envisaged to be available with the central server (130). In other physical environments, locations of the interface module (410) and the signal processing module (420) may vary depending upon specific implementations. The data interface module (410) is configured to receive the electrophysiological signals from the first device (110). The signal processing module (420) is configured to normalize the sampling frequency of the received electrophysiological signals, to obtain the first

signal. In one embodiment of the invention, for normalizing the sampling frequency of the received electrophysiological signals the signal processing module (420) is further configured to up-sample or down-sample the received electrophysiological signals. Also, the signal processing module (420) is configured to up-sample or down-sample the received electrophysiological signals to a sampling frequency in a range of 990 Hz to 1010 Hz.
Further, the signal processing module (420) is configured to filter the first signal using the first filter, to obtain the filtered signal and smoothen the filtered signal by using the second filter, to obtain the smoothened signal. Further, the signal processing module (420) is configured to subtract the smoothened signal from the filtered signal to derive the second signal and down-sample the second signal to the sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals, to obtain the down-sampled signal.
Additionally, the signal processing module (420) is configured to flatten the down-sampled signal, to obtain the flattened signal, up-sample and amplify the flattened signal to match the sampling frequency of the smoothened signal and correct for the attenuation of peaks in the smoothed signal respectively, add the up-sampled and amplified flattened signal to the smoothened signal to derive the third signal, down-sample the third signal to the sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals and resample the down-sampled third signal to a predetermined sampling frequency, to obtain the resultant signal having reduced noise.
The resultant signal so obtained in significantly free of baseline wander, power line interference, notches and unintended artefacts.
In some examples, the systems described herein, may include one

or more processors, one or more forms of memory, one or more input devices/interfaces, one or more output devices/interfaces, and machine-readable instructions that when executed by the one or more processors cause the system to carry out the various operations, tasks, capabilities, etc., described above.
In some embodiments, the disclosed techniques can be implemented, at least in part, by computer program instructions encoded on a non-transitory computer-readable storage media in a machine-readable format, or on other non-transitory media or articles of manufacture. Such computing systems (and non-transitory computer-readable program instructions) can be configured according to at least some embodiments presented herein, including the processes described in above description.
The programming instructions can be, for example, computer executable and/or logic implemented instructions. In some examples, a computing device is configured to provide various operations, functions, or actions in response to the programming instructions conveyed to the computing device by one or more of the computer readable medium, the computer recordable medium, and/or the communications medium. The non-transitory computer readable medium can also be distributed among multiple data storage elements, which could be remotely located from each other. The computing device that executes some or all of the stored instructions can be a microfabrication controller, or another computing platform. Alternatively, the computing device that executes some or all of the stored instructions could be remotely located computer system, such as a server.
In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example,

Java, C, or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of computer-readable medium or other computer storage device.
Further, while one or more operations have been described as being performed by or otherwise related to certain modules, devices or entities, the operations may be performed by or otherwise related to any module, device or entity. As such, any function or operation that has been described as being performed by a module could alternatively be performed by a different server, by the cloud computing platform, or a combination thereof.
Further, the operations need not be performed in the disclosed order, although in some examples, an order may be preferred. Also, not all functions need to be performed to achieve the predetermined advantages of the disclosed system and method, and therefore not all functions are required.
Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the embodiments shown along with the accompanying drawings but is to be providing broadest scope of consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims.

We Claim:
1. A method (200) for reducing noise in electrophysiological signals, the method (200) comprising steps of:
receiving (202) the electrophysiological signals from a first device (110) and normalizing a sampling frequency of the received electrophysiological signals, to obtain a first signal;
filtering (204) the first signal using a first filter, to obtain a filtered signal;
smoothening (206) the filtered signal by using a second filter, to obtain a smoothened signal;
subtracting (208) the smoothened signal from the filtered signal to derive a second signal;
down-sampling (210) the second signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals, to obtain a down-sampled signal;
flattening (212) the down-sampled signal, to obtain a flattened signal;
up-sampling (214) and amplifying the flattened signal to match a sampling frequency of the smoothened signal and correct for the attenuation of peaks in the smoothed signal respectively;
adding (216) the up-sampled and amplified flattened signal to the smoothened signal to derive a third signal;
down-sampling (218) the third signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals; and
resampling (220) the down-sampled third signal to a predetermined sampling frequency, to obtain a resultant signal having reduced noise.

2. The method (200) as claimed in claim 1, wherein the first filter is Butterworth filter.
3. The method (200) as claimed in claim 2, wherein the Butterworth filter is a 1-Hz high pass third order Butterworth filter.
4. The method (200) as claimed in claim 1, wherein the normalizing of the sampling frequency of the received electrophysiological signals comprises one up-sampling or down-sampling of the received electrophysiological signals.
5. The method (200) as claimed in claim 4, wherein the up-sampling or down-sampling of the received electrophysiological signals is performed to a sampling frequency in a range of 990 Hz to 1010 Hz.
6. The method (200) as claimed in claim 1, wherein the second filter is Savitzky-Golay filter with a sampling frequency dependent window length and first order polynomials.
7. A system (400) for reducing noise in electrophysiological signals, the system (400) comprising:
a data interface module (410); and a signal processing module (420);
wherein the data interface module (410) is configured to:
receive the electrophysiological signals from a first device (110);
wherein the signal processing module (420) is configured to:
normalize a sampling frequency of the received
electrophysiological signals, to obtain a first signal;
filter the first signal using a first filter, to obtain a filtered signal; smoothen the filtered signal by using a second filter, to obtain

a smoothened signal;
subtract the smoothened signal from the filtered signal to derive a second signal;
down-sample the second signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals, to obtain a down-sampled signal;
flatten the down-sampled signal, to obtain a flattened signal;
up-sample and amplify the flattened signal to match the sampling frequency of the smoothened signal and correct for the attenuation of peaks in the smoothed signal respectively;
add the up-sampled and amplified flattened signal to the smoothened signal to derive a third signal;
down-sample the third signal to a sampling frequency that is equal to or less than one third of the sampling frequency of the received electrophysiological signals; and
resample the down-sampled third signal to a predetermined sampling frequency, to obtain a resultant signal having reduced noise.
8. The system (400) as claimed in claim 7, wherein the first filter is Butterworth filter.
9. The system (400) as claimed in claim 8, wherein the Butterworth filter is a 1-Hz high pass third order Butterworth filter.
10. The system (400) as claimed in claim 7, wherein for normalizing the sampling frequency of the received electrophysiological signals the signal processing module (420) is further configured to up-sample or down-sample the received electrophysiological signals.
11. The system (400) as claimed in claim 10, wherein the signal processing module (420) is configured to up-sample or down-

sample the received electrophysiological signals to a sampling frequency in a range of 990 Hz to 1010 Hz.
12. The system (400) as claimed in claim 7, wherein the second filter is Savitzky-Golay filter with a sampling frequency dependent window length and first order polynomials.