CLIAMS:WE CLAIM:

1. A method for identifying an individual using Electrocardiogram (ECG) and Plethysmograph (PPG) signals, the method comprising:
recording, by a processor, first Electrocardiogram (ECG) signals and first Plethysmograph (PPG)signals of an individual, at a first phase;
extracting, by the processor, first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively;
capturing, by the processor, second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second phase;
estimating, by the processor, second Electrocardiogram (ECG) features from the second PPG features captured and selecting effective first ECG features using the Maximum Information Co-efficient (MIC) technique; and
matching, by the processor, the second ECG features estimated with the first ECG features extracted to identify the individual.

2. The method of claim 1, further comprising capturing preferences of the individual at the first phase to personalize a device associated with the individual.

3. The method of claim 2, further comprising executing the preferences upon identifying the individual.

4. The method of claim 2, wherein the device comprises at least one of: an electronic device and a vehicle.

5. The method of claim 4, wherein the personalization of the preferences comprises at least one of: adjusting playlist of songs, video and FM /TV stations, speaker volume, displaying of a dashboard, adjusting height of a seat, security settings, navigation display settings, and an engine response.

6. A system for identifying an individual using Electrocardiogram (ECG) and Plethysmograph (PPG) signals, the system comprising:
a memory; and
a processor coupled to the memory, wherein the processor executes program instructions stored in the memory, to:
record first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual, at a first phase;
extract first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively;
capture second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second phase;
estimate second Electrocardiogram (ECG) features from the second PPG features and select effective first ECG features using a Maximum Information Co-efficient (MIC) technique; and
match the second ECG features extracted with the first ECG features extracted to identify the individual.

7. The system of claim 6, wherein the processor further executes the program instructions to capture preferences of the individual at the first phase to personalize a device associated with the individual.

8. The system of claim 6, wherein the processor further executes the program instructions to execute the preferences upon identifying the individual.

9. The system of claim 6, wherein the device comprises at least one of: an electronic device and a vehicle.

10. The system of claim 9, wherein the personalization of the preferences comprises at least one of: adjusting play list of songs, video and FM /TV stations, speaker volume, displaying of a dashboard, adjusting height of a seat, security settings, navigation display settings, and engine response.

11. A non-transitory computer readable medium embodying a program executable in a computing device for identifying an individual using Electrocardiogram (ECG) and Plethysmograph (PPG) signals, the program comprising:
a program code for recording first Electrocardiogram (ECG) signals and first Plethysmograph (PPG)signals of an individual, at a first phase;
a program code for extracting first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively;
a program code for capturing second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second phase;
a program code for extracting second Electrocardiogram (ECG) features from the second PPG features captured and selecting effective first ECG features using a Maximum Information Co-efficient (MIC) technique; and
a program code for matching the second ECG features extracted with the first ECG features extracted to identify the individual. ,TagSPECI:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR IDENTIFYING AN INDIVIDUAL USING ELECTROCARDIOGRAM (ECG) OR PLETHYSMOGRAPH (PPG) SIGNALS


APPLICANT:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India


The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS REFERENCE TO RELATED APPLICATIONS
[001] The present application does not claim priority to any other Patent Application.

TECHNICAL FIELD
[002] The present disclosure in general relates to a field of identifying an individual using biological signal of the individual. More particularly, the present disclosure relates to a system and a method for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signals.

BACKGROUND
[003] Typically, bio-signals of an individual are captured and processed to determine health of the individual. Generally, Electrocardiography (ECG) and Photoplethysmography or Plethysmography (PPG) is used to monitor health, such as cardiac system of the individual. Typically, ECG is used to measure bio-potential generated by the cardiac system. PPG is used to measure rate of blood flow as controlled by the heart’s pumping action using a photo or light-based technology.
[004] As known, the ECG and PPG may be used to identify an individual. Typically, a biometric system is used to identify the individual. For example, biometrics, such as finger print of the individual may be captured to identify the individual. Generally, at first or enrolment phase, raw data corresponding to finger print, ECG and/or PPG may be stored. Subsequently, finger print, ECG and PPG signals of the individual are collected at a later or testing or probing phase and are compared with the data stored. Additionally, the raw data may be encrypted to enhance security of during authentication. For example, finger print of the individual may be stored at the enrolment phase and may be compared with the finger print captured at the probing phase.

SUMMARY
[005] This summary is provided to introduce concepts related to systems and methods for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signal and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[006] In one implementation, a method for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signals is disclosed. The method comprises recording, by a processor, first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual, at a first or enrolment phase. The method further comprises extracting, by the processor, first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively. The method further comprises capturing, by the processor, second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second or probe phase. The method further comprises estimating, by the processor, second Electrocardiogram (ECG) features from the second PPG features captured and selecting the effective ECG features using a Maximum Information Co-efficient (MIC) technique. The method further comprises matching, by the processor, the second ECG features extracted with the first ECG features estimated to identify the individual.
[007] In one implementation, a system for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signals is disclosed. The system comprises a memory and a processor coupled to the memory. The processor executes program instructions stored in the memory to record first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual, at a first or enrolment phase. The processor further executes the program instructions to extract first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively. The processor further executes the program instructions to capture second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second or probe phase. The processor further executes the program instructions to estimate the ECG features using the second PPG features, or vice versa, by linking the first ECG features and the second PPG features using a Maximum Information Co-efficient (MIC) technique. The processor further executes the program instructions to match the second ECG features estimated with the first ECG features extracted to identify the individual. The processor further executes the program instructions to capture preferences of the individual at the first phase to personalize a device associated with the individual.
[008] In one implementation, a non-transitory computer readable medium embodying a program executable in a computing device for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signals is disclosed. The program comprises a program code for recording first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual, at a first or enrolment phase. The program further comprises a program code for extracting first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively. The program further comprises a program code for capturing second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second or probe phase. The program further comprises a program code for estimate the second Electrocardiogram (ECG) features from the second PPG features captured and select effective ECG features using a Maximum Information Co-efficient (MIC) technique. The program further comprises a program code for matching the second ECG features estimated with the first ECG features extracted to identify the individual.

BRIEF DESCRIPTION OF DRAWINGS
[009] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the Drawings to refer like/similar features and components.
[010] FIG. 1 illustrates a network implementation of a system for identifying an individual using Electrocardiogram (ECG) signal or Plethysmograph (PPG) signal, in accordance with an embodiment of the present disclosure.
[011] FIG. 2 illustrates the system, in accordance with an embodiment of the present disclosure.
[012] FIG. 3 shows a method for capturing ECG and PPG signal at first or enrolment phase, and capturing PPG signal at second or probe phase, in accordance with an embodiment of the present disclosure.
[013] FIG. 4 shows a flowchart for identifying an individual using Electrocardiogram (ECG) and Plethysmograph (PPG), in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[014] The present disclosure relates to a system and a method for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signals. The system may be implemented as a biosense device. The system may comprise two phases: a first phase or an enrolment phase and a second or probe phase. In the first phase; first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual may be recorded. After recording, first ECG features and first PPG features may be extracted from the first ECG signals and the first PPG signals respectively.
[015] At the second phase, second Plethysmograph (PPG) features may be captured by recording second PPG signals of the individual. After capturing, second Electrocardiogram (ECG) features may be estimated from the second PPG features and effective features of ECG are selected using a Maximum Information Co-efficient (MIC) technique. Upon extracting, the second ECG features may be matched with the first ECG features to identify the individual. In other words, the second ECG features estimated for the individual from PPG signals is compared with the first ECG features extracted for authenticating the individual.
[016] While aspects of described system and method for identifying an individual using Electrocardiogram (ECG) and Plethysmograph (PPG) signal may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
[017] Referring now to FIG. 1, a network implementation 100 of a system 102 for identifying an individual using Electrocardiogram (ECG) and Plethysmograph (PPG) signal is illustrated, in accordance with an embodiment of the present disclosure. The system 102 may record first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual, at a first phase. Further, the system 102 may extract first ECG features and first PPG features from the first ECG signals and the first PPG signals respectively. The system 102 may capture second Plethysmograph (PPG) features by recording second PPG signals of the individual, at a second phase. Furthermore, the system 102 may estimate second Electrocardiogram (ECG) features from the second PPG features captured and effective features of ECG are selected using a Maximum Information Co-efficient (MIC) technique. Subsequently, the system 102 may match the second ECG features estimated with the first ECG features extracted to identify the individual.
[018] Although the present disclosure is explained by considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, cloud, automobile or vehicle and the like. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user devices 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[019] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[020] Referring now to FIG. 2, the system 102 is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one 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 at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[021] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the user devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[022] The memory 206 may include any 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.
[023] In one implementation, at first, the user may use the client device 104 to access the system 102 via the I/O interface 204. The working of the system 102 may be explained in detail using FIG. 2 and FIG. 3. The system 102 may be used for identifying an individual using Electrocardiogram (ECG) and/or Plethysmograph (PPG) signals. In order to identify an individual, at a first phase, the system 102 may record Electrocardiogram (ECG) signals and Plethysmograph (PPG) signals of the individual.
[024] Referring to FIG. 3, a method 300 for capturing ECG and PPG signals at first phase, and capturing PPG signal at second phase, is shown. In one example, physiological information such as the ECG signals and the PPG signals may be captured or recorded using a biosense device. Specifically, the ECG signals and the PPG signals may be recorded using an external or an internal device or combination of external and internal that has been attached to at least one of: a vehicle, a mobile phone, a computer, entrance door, ATM, bank, entertainment room and so on. According to one exemplary embodiment of the present disclosure, at first phase, the ECG signals may be measured using a biosense cardiac monitoring device. The biosense cardiac monitoring device may measure a single lead (lead I or II or so on) -Electrocardiogram (ECG) signal that can be integrated to various vehicular parts. For example, the biosense cardiac monitoring device in a vehicle may record the ECG signals of the individual from one of: a steering wheel, a seat, a gear shift lever, and so on.
[025] As known, the ECG signals recorded are represented by an ECG waveform. In one example, the ECG signals may be recorded using a single lead ECG system (not shown). In order to record the ECG signals, the single lead ECG system may be placed on a side of the steering wheel to measure a low voltage signal, approximately 1mV. As the ECG signal is measured at low voltage signal, the ECG signal may be amplified. In one example, the ECG signal may be amplified using a differential amplifier. Upon amplifying, the ECG signal may be filtered to remove noise caused due to surroundings in which the ECG signals being acquired or captured or recorded. Further, the ECG signals may be filtered to collect the ECG signal at a desired frequency. Subsequently, the ECG signal filtered may be passed as an input signal to a microcontroller (not shown)to convert the ECG signal, in analog, to digital using an Analog-to-Digital converter (ADC). In one example, the ECG signal may be passed at a sample rate of 250Hz to convert to digital. Further, the ECG signals may be pre-processed (304) to extract ECG features. Upon extracting the ECG features, feature vector (306) may be applied.
[026] The PPG signals may be recorded in mV using a surface electrode i.e., single lead. Further, the PPG signal may be amplified to Voltage signal. Subsequently, the PPG signals may be pre-processed to extract the PPG features (F1, F2, F3, and so on). In one example, the PPG features extracted for the individual is shown in Table 1. Specifically, Table 1 shows the ECG features extracted for the individual at the first phase, i.e., enrolment or training phase. The PPG features F(x) may include a set of time domain features (Ft) or a set of frequency domain features (Ff), or both. The PPG features may include 11 time domain features (Ft = {f1, f2, f3, .., f11}), such as:
[027] (1) peak to peak interval (Tsn +1 - Tsn), (2) pulse interval (Tvn +1 - Tvn), (3) pulse height (Asn - Avn), (4) crest time (Tsn - Tvn), (5) delta time (Tdn - Tsn), (6) trough to notch time (Tdn - Tvn), (7) falling time (Tvn +1 - Tsn), (8) notch to trough time (Tvn +1 - Tdn), (9) rising slope ((Asn - Avn)/(Tsn - Tvn)), (10) falling slope ((Avn +1 - Asn)/(Tvn +1 - Tsn)) and (11) area under a complete cycle.
[028] Further, the PPG features may include four frequency domain features (Ff= {f12, f13, f14, f15}), such as 1) dominant peak location, 2) distance between dominant and its immediate peak, 3) spectral centroid and 4) width of dominant peak region.
[029] Table 1 PPG Feature for an individual

Feature
F1 40
F2 2
F3 3.06
F4 0.26
F5 -77.00
F6 2
F7 124
F8 1.13
F9 1.7
F10 0.70
F11 1.4Hz
F12 16
F13 1.2207
F14 1.5242
F15 2.2


Table 1
[030] In the Table 1, the time is in ms and height is in volt.

[031] Table 2: ECG Features for the individual

User F1 F2 F3 F4 F5 F6 F7 F8 F9
1 0.038 0.814 39.042 93.222 44.861 27.556 44.861 39.042 138.806
Table 2
[032] Similar to recording and pre-processing of the PPG signals, ECG signals of the individual may be recorded and ECG features may be extracted as shown in Table 2.
[033] At first phase, PPG signals of the individual may be recorded (302). In one example, the PPG signals of the individual may be recorded using a Photoplethysmography (PPG) device (not shown). In one example, the PPG device may be a mobile based application or any other commercial available PPG device. As known, PPG is an electro-optic technique to measure pulse wave of vessels. The PPG signals may be measured using a pulse oximeter, which considers relative absorption of Hemoglobin and Oxyhemoglobin to non-invasive measure of arterial oxygen saturation (SpO2) using the dual-wavelength illumination (LED). Specifically, the change in volume caused by pressure pulse may be detected by illuminating skin with the light from a light-emitting diode (LED) and then measuring the amount of light either transmitted or reflected to a photodiode. As known, each cardiac cycle appears as a peak, and the shape of PPG signals in a waveform is captured.
[034] After detecting the dual wavelength illumination from the LED, the PPG signal in analog form may be converted into digital using an Analog-to-Digital Converter (ADC). Further, the PPG signals may be pre-processed (304) to extract PPG features (304).
[035] After extracting, the ECG features and the PPG features may be classified (320) using one or more parametric statistical models, non-parametric statistical models, clustering models, nearest neighbour models, regression methods, and artificial neural networks, and so on.
[036] For the individual, corresponding to the ECG features and/or the PPG features extracted, preferences of the individual may be captured at the first phase. The preferences may be captured to personalize settings of a device used by the individual. For example, consider a vehicle steering wheel is used to collect biological signal of the individual. Based on the ECG features and/or the PPG features extracted at the first phase, the personalization for the vehicle may be set for the individual. For instance, the personalization may include a seat height adjusted for the individual, a play list of songs or video selected by the individual, an engine response chosen and so on. Similarly, if the individual is using an electronic device to capture the ECG features and the PPG features, preferences of the individual in the electronic device may be set. For instance, the individual may set preferences of play list of songs, video and FM /TV stations, speaker volume, security settings, navigation display settings and so on.
[037] After enrolling, the PPG features of the individual may be captured at a second phase, i.e., probing phase. The PPG features may be captured for a period of one minute or more. It is to be understood that the second phase may not take place at a time of the first phase. Rather, the second phase may take place with a gap of one hour, one day, or any time after the first phase. In other words, the first and second phase may be performed with random time intervals therebetween. Referring to FIG.3, the PPG signals may be captured (312) at the second phase, similar to the first phase (for either ECG or PPG). After capturing, the PPG signals may be pre-processed (314) as explained earlier. Subsequent to pre-processing, the PPG features may be extracted (316). In the second phase, only the PPG signals are captured and the ECG features are estimated from the PPG signals. Specifically, the PPG features are extracted from the individual at the second phase and the ECG features are estimated. The effective ECG features are estimated using a Maximum Information Co-efficient (MIC) technique or any other feature selection technique. In one example, the MIC technique may be used to link and obtain the ECG features and the PPG features. The effective feature selection using the MIC may be obtained as:

Where F={Ft+Ff}
where B(n) is a function of sample size (n) (usually B(n)= n0.6). Where G is the grid of size(x,y)

For different distributions of G, M (F) is given by
I –mutual information of F for pair wise data (x,y) is a gird G
[038] If the MIC of nthPPG feature fn with respect to an ECG parameter are wn, then a gain factor Gn may be calculated using a sigmoid function.

[039] If the MIC values (= 0.5) are more than the gain factor and are close to 1, then M(x) ={F3, F5, F6, F8, F13, F15}.
[040] The M(x) PPG features may hold good for certain ECG features, such as RR interval by producing high Pearson coefficient (above 0.8 in magnitude) values. Therefore, the RR interval values of the ECG features may be estimated using the PPG features by linear regression. However, estimating the PR, QRS and QT interval parameters of the ECG features may be difficult using the PPG features. In order to predict course values of the PR, QRS and QT interval parameters using the PPG features, a classifier algorithm such as (Support Vector Machine) SVM may be used to predict morphological features, such as duration of P, QRS and T wave, intervals of PR, ST and QT and amplitudes of P, R and T waves as shown in Table3. For example, the PR of ECG parameter may be estimated from {F4,F6,F9}, QRS feature parameter from {F4,F5,F6} and QT from {F1,F2,F6,F10,F12,F14} of the PPG feature vectors.
[041] Table 3: Morphological features
MORPHOLOGY TIME BASEDmsec
HR 67
P Duration 0.105
QRS Duration 0.088
T Duration 0.21
PR Interval 0.18
QT Interval 0.425
ST Interval 0.36
P Amplitude 0.1
R Amplitude 0.64
T Amplitude 0.29
Table 3
[042] After estimating the ECG features using the PPG features captured at the second phase, the ECG features are matched with the ECG features extracted at the first phase. The ECG features estimated at the second phase is matched with the ECG features extracted at the first phase to identify the individual (322). Considering the example of individual in the vehicle, the ECG features estimated at the second phase may be matched with the ECG features extracted at the first phase. If the ECG features estimated at the second phase matches with the ECG features extracted at first phase, the individual is provided to access the vehicle (324). If the ECG features estimated in the second phase do not match with the ECG features extracted at first phase, then access may be denied for the individual (326).
[043] If the ECG features estimated at the second phase matches with the ECG features extracted at the first phase, then the preferences set by the individual may be activated or triggered. For example, if the individual has set preferences for particular playlist during the enrolment phase, upon identifying the individual, the songs in the playlist may be played. Further, if the ECG features captured at the second phase do not match with the ECG features extracted at first phase, then an alarm may be raised to indicate that the individual is not identified.
[044] The present disclosure may be used for biometric profile and personalization settings of different users or drivers or mobile phone users and so on. Further, the system 102 may be used to identity and personalize settings by matching a profile of the individual registered at the first phase (ECG features and PPG features) with the PPG features collected at the second phase. If the profile does not match, indicating an intrusion, the system 102 may generate an alert in a form of visual or sound. The MIC is used to select an ECG feature in order to increase the accuracy of the authentication. Further, the MIC avoids true false ratio for the identification.
[045] Further, the ECG signals and the PPG signals recorded may be used to monitor health of the individual. Furthermore, the ECG signals and the PPG signals recorded may be transmitted to a Decision Support System (DSS) application present in a mobile/smart phone. Subsequently, the DSS may upload information to a centralized server to perform further analysis.
[046] The ECG features and the PPG features recorded at the first phase and the second phase are stored in the memory 206. The ECG features and the PPG features are stored in the memory 206 to avoid tampering of data.
[047] Although the embodiment are explained considering capturing of PPG signals at the second stage to estimate ECG features and to further identify the individual, it must be noted that ECG signals may be captured at the second stage and PPG features may be estimated. After estimating the PPG features using the ECG features at the second stage, the PPG features may be matched with the PPG features extracted at the first stage to identify the individual.
[048] Referring now to FIG. 4, a method 400 for identifying an individual using Electrocardiogram (ECG) features or Plethysmograph (PPG) signals is shown, in accordance with an embodiment of the present disclosure. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[049] The order in which the method 400 is described and is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400 or alternate methods. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 400 may be implemented in the above-described system 102.
[050] At step/block 402, first Electrocardiogram (ECG) signals and first Plethysmograph (PPG) signals of an individual may be recorded, at a first phase.
[051] At step/block 404,first ECG features and first PPG features may be extracted from the first ECG signals and the first PPG signals respectively.
[052] At step/block 406, second Plethysmograph (PPG) features may be captured by recording second PPG signals of the individual, at a second phase.
[053] At step/block 408, second Electrocardiogram (ECG) features may be estimated from the second PPG features captured and effective features of ECG may be selected using a Maximum Information Co-efficient (MIC) technique.
[054] At step/block 410, the second ECG features estimated may be matched with the first ECG features extracted to identify the individual.
[055] Although implementations of system and method for identifying an individual using Electrocardiogram (ECG) or Plethysmograph (PPG) signals have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for identifying an individual using Electrocardiogram (ECG) and/or Plethysmograph (PPG) signals.