TECHNICAL FIELD

[0001] The present disclosure relates to the field of ECG monitoring, and
more particularly the present disclosure relates to a system for remotely detecting heart rate condition of an individual.
BACKGROUND
[0002] Background description includes information that may be useful in
understanding the present invention. It is not an admission that any of the
information provided herein is prior art or relevant to the presently claimed
invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Recent developments in mobile applications and sensor based
technology in health care system has gained remarkable progress in the past decade for automated diagnosis in health care system. Still, the recent research outcome is limited with sustainability issues in real time scenario in modern healthcare systems. However, traditional ECG systems are limited in terms of remote access, non-transformable and portability. More specifically, all the existing approaches uses 12 leads for ECG acquisition hence it was more costly. Therefore, it is difficult for doctor to diagnose a critical patient remotely using traditional ECG system, and use of high voltage equipment in general ECG monitoring process is annoying to the patient having pacemaker. Thus, there exists an ample of scope to suggest a handheld device for real time ECG monitoring system using wireless mobile App, which enables remote diagnosis of patients with less cost and less time.
[0004] There is, therefore, a need of a portable ECG monitoring device
that can be used even remotely to diagnose a patient who cannot come to the hospitals.
OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one
embodiment herein satisfies are as listed herein below.

[0006] It is an object of the present disclosure to provide a system for
detecting heart rate condition of an individual which can be used in remotely.
[0007] It is an object of the present disclosure to provides a system for
remotely detecting heart rate condition of an individual which is cost effective and
simple to use.
[0008] It is an object of the present disclosure to provide a system for
detecting heart rate condition of an individual which his developed to save rural
life as they cannot reach in time and it provide in very low cost.
[0009] It is an object of the present disclosure to provide a system for
detecting heart rate condition of an individual that can calculate the number of
heart beats per minute (bpm) and beat count based on obtained ECG signal form.
SUMMARY
[0010] The present disclosure relates to the field of ECG monitoring, and
more particularly the present disclosure relates to a system for remotely detecting heart rate condition of an individual.
[0011] An aspect of the present disclosure pertains to a system for
detecting heart rate condition of an individual. The system includes a device configured to be worn by the individual, and the device is configured for sensing heart rate of the individual and correspondingly generates a first set of signals pertaining to the heart rate of the individual; a processing unit operatively configured with the device. The processing unit includes a memory associated with the processing unit for storing instructions which when executed cause the processing unit to receive, from the device, the first set of signals. Extract, from the first set of signals, time domain and amplitude domain features and correspondingly generate a second set of signals. Save, the first set of signals and the second set of signals in a training and testing dataset associated with the processing unit; and validate, using deep learning technique, the time domain and amplitude domain extracted feature and correspondingly generating a third set of signals pertaining to heart rate condition of the individual. A mobile computing

unit operatively configured to the device and processing unit. The mobile
computing device is configured to receive the third set of signals.
[0012] In an aspect, the device may include any or combination of
monitoring electrodes with foam disposable backing, ECG sensor, and a
communication unit, and wherein the communication unit is configured to
communicate the first set if signals to the processing unit.
[0013] In an aspect, the time domain features may comprise any or
combination of QRS complex based wave feature, Pre-RR-interval, Post-RR-
interval, Average RR-interval, and Average RR-interval.
[0014] In an aspect, the amplitude domain features may comprise any or
combination of P wave, Q wave, R wave, S wave.
[0015] In an aspect, the extraction of feature may comprise employing a
plurality of high-pass filters of a first predefined frequency for time cut-off of
low-pass frequency with static at a second pre-defined frequency; and calculating
three conventional indexes for each of the plurality of high-pass filters such that a
root mean square of the amplitude of any or combination of first set of signals,
second set of signals, and third set of signals is same as that of QRS complex.
[0016] In an aspect, the plurality of high-pass filters may comprise
bidirectional Butterworth filters.
[0017] In an aspect, the communication unit may comprise any or
combination of Bluetooth, and Wi-Fi.
[0018] Various objects, features, aspects and advantages of the inventive
subject matter will become more apparent from the following detailed description
of preferred embodiments, along with the accompanying drawing figures in which
like numerals represent like components.
BRIEF DESCRIPTION OF DRAWINGS
[0019] The accompanying drawings are included to provide a further
understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the

principles of the present disclosure. The diagrams are for illustration only, which
thus is not a limitation of the present disclosure.
[0020] In the figures, similar components and/or features may have the
same reference label. Further, various components of the same type may be
distinguished by following the reference label with a second label that
distinguishes among the similar components. If only the first reference label is
used in the specification, the description is applicable to any one of the similar
components having the same first reference label irrespective of the second
reference label.
[0021] FIG. 1 illustrates an exemplary system for remotely detecting heart
rate condition of an individual, in accordance with an embodiment of the present
disclosure.
[0022] FIG. 2 illustrates exemplary representation of different components
present in the proposed device for monitoring heart rate, in accordance with an
embodiment of the present disclosure.
[0023] FIG. 3 illustrates exemplary representation of output on a mobile
computing device configured with the proposed system, in accordance with an
embodiment of the present disclosure.
[0024] FIG. 4 illustrates exemplary representation of a method for
remotely detecting heart rate condition of an individual, in accordance with an
embodiment of the present disclosure.
[0025] FIG. 5 illustrates exemplary representation of feature extraction
process, in accordance with an embodiment of the present disclosure.
[0026] FIG. 6 illustrates exemplary representation of training and testing
observation in deep learning technique, in accordance with an embodiment of the
present disclosure.
DETAILED DESCRIPTION
[0027] The following is a detailed description of embodiments of the
disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail

offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0028] In the following description, numerous specific details are set forth
in order to provide a thorough understanding of embodiments of the present
invention. It will be apparent to one skilled in the art that embodiments of the
present invention may be practiced without some of these specific details.
[0029] The present disclosure relates to the field of ECG monitoring, and
more particularly the present disclosure relates to a system for remotely detecting heart rate condition of an individual.
[0030] The present disclosure elaborates upon a system for detecting heart
rate condition of an individual. The system includes a device configured to be worn by the individual, and the device is configured for sensing heart rate of the individual and correspondingly generate a first set of signals pertaining to the heart rate of the individual; a processing unit operatively configured with the device. The processing unit includes a memory associated with the processing unit for storing instructions which when executed cause the processing unit to receive, from the device, the first set of signals. Extract, from the first set of signals, time domain and amplitude domain features and correspondingly generate a second set of signals. Save, the first set of signals and the second set of signals in a training and testing dataset associated with the processing unit; and validate, using deep learning technique, the time domain and amplitude domain extracted feature and correspondingly generating a third set of signals pertaining to heart rate condition of the individual. A mobile computing unit operatively configured to the device and processing unit. The mobile computing device is configured to receive the third set of signals.
[0031] In an embodiment, the device can include any or combination of
monitoring electrodes with foam disposable backing, ECG sensor, and a communication unit, and wherein the communication unit is configured to communicate the first set if signals to the processing unit.

[0032] In an embodiment, the time domain features can include any or
combination of QRS complex based wave feature, Pre-RR-interval, Post-RR-
interval, Average RR-interval, and Average RR-interval.
[0033] In an embodiment, the amplitude domain features can include any
or combination of P wave, Q wave, R wave, S wave.
[0034] In an embodiment, the extraction of feature can include employing
a plurality of high-pass filters of a first predefined frequency for time cut-off of
low-pass frequency with static at a second pre-defined frequency; and calculating
three conventional indexes for each of the plurality of high-pass filters such that a
root mean square of the amplitude of any or combination of first set of signals,
second set of signals, and third set of signals is same as that of QRS complex.
[0035] In an embodiment, the communication unit can include any or
combination of Bluetooth, and Wi-Fi.
[0036] In an embodiment, the plurality of high-pass filters can include
bidirectional Butterworth filters.
[0037] In an embodiment, the training and testing databasecan include but
not limited to a server, and cloud.
[0038] FIG. 1 illustrates an exemplary system for remotely detecting heart
rate condition of an individual, in accordance with an embodiment of the present
disclosure.
[0039] FIG. 2 illustrates exemplary representation of different components
present in the proposed device for monitoring heart rate, in accordance with an
embodiment of the present disclosure.
[0040] As illustrated in FIG. 1, the proposed system for remotely
monitoring or detecting heart rate condition of an individual 102 can include a
device configured to be worn by the individual, and the device can be configured
for sensing heart rate 104 of the individual 102 and correspondingly generate a
first set of signals pertaining to the heart rate of the individual 102. A processing
unit 106 can be operatively configured with the device. The processing unit 106
can include a memory associated with the processing unit 106 for storing
instructions which when executed can cause the processing unit 106 to receive,

from the device, the first set of signals. Extract, from the first set of signals, time domain and amplitude domain features and correspondingly generate a second set of signals. Save, the first set of signals and the second set of signals in a training and testing dataset in a database 114 (also referred as server 114, herein) associated with the processing unit. Validate, using deep learning technique, the time domain and amplitude domain extracted feature and correspondingly generating a third set of signals pertaining to heart rate condition of the individual 102.
[0041] In an embodiment, the processing unit 106 can include a processor,
and a memory associated with the processor 106. A mobile computing unit 108 can be operatively configured to the device and processing unit 106 and the mobile computing device 108 can be configured to receive the third set of signals. The device can include different components (as shown in FIG. 2) that can include nut not limited to monitoring electrodes 202 with foam disposable backing 204, ECG sensor 104, and a communication unit. The communication unit can be configured to communicate the first set if signals to the processing unit 106.
ECG Sensor Module: Signal can be detected and view using standard ECG Sensor Module such as AD8232
Electrode and wire: Signals can be collected after attaching standard Electrode (3M Monitoring Electrode with Foam Backing) and standard wire.
Standard Bluetooth: Signal can be acquired from user via electrodes and connected corresponding sensor but collected signal can be transferred to the processing unit via Bluetooth.
[0042] FIG. 3 illustrates exemplary representation of output on a mobile
computing device configured with the proposed system, in accordance with an embodiment of the present disclosure.
[0043] As illustrated, in an embodiment, the mobile computing device 108
can include but not limited to a mobile phone, smartphone, tablet, laptop, PDA etc. The mobile computing device 108 can include a display unit that can be used

to show output results of the proposed system for remote monitoring the heart rate of the individual 102. The output can be shown in the form of but not limited to waveform.
[0044] FIG. 4 illustrates exemplary representation of a method for
remotely detecting heart rate condition of an individual, in accordance with an embodiment of the present disclosure.
[0045] FIG. 5 illustrates exemplary representation of feature extraction
process, in accordance with an embodiment of the present disclosure.
[0046] FIG. 6 illustrates exemplary representation of training and testing
observation in deep learning technique, in accordance with an embodiment of the present disclosure.
[0047] As illustrated, in an embodiment, the proposed method 400 can
include different steps: the device can include an ECG sensor module 104,
Bluetooth, 3M Monitoring Electrode 202 with foam disposable Backing 204 of
three pieces. After switch on, ECG wave can start generating from wearable
electrode and via Bluetooth, and start pre-processing stage for cleaning of raw
signal. The pre-processing stage can include removing noise from the signals (also
referred as first set of signals, herein) generated from the device.
[0048] In an embodiment, after per-processing step done, the first set of
signals can be transferred to feature extraction stage (as shown in FIG. 5) in both time and amplitude domain. In an embodiment, the time domain features can include any or combination of QRS complex based wave feature, Pre-RR-interval, Post-RR-interval, Average RR-interval, and Average RR-interval. In an embodiment, the amplitude domain features can include any or combination of P wave, Q wave, R wave, S wave.
[0049] In an embodiment, the amplitude extraction of feature can include
employing a two high-pass filters of a first predefined frequency for time cut-off of low-pass frequency with static at a second pre-defined frequency. The first pre-defined frequency for the two high-pass filters can be 20Hz and 40 Hz, and the second pre-defined frequency can be 250 Hz. Calculating three conventional indexes for each of the plurality of high-pass filters such that a root mean square

of the amplitude of any or combination of first set of signals, second set of
signals, and third set of signals is same as that of QRS complex.
[0050] In an embodiment, after feature extraction analysis, ECG signals
can be through proper ratio for training and testing purpose in order to validate classification accuracy (As shown in FIG. 6). After successfully evaluation of feature extraction of each signal, finally extracted feature are feed to deep learning technique that can include but not limited to multilayer deep neural network (D-CCN) for final classification. After successfully evaluation final classification, now all processed data can be transfer to cloud server for data storage by created mobile App. Final result can be display in display output screen of mobile with measure indicator.
[0051] Moreover, in interpreting the specification, all terms should be
interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C ....and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0052] While the foregoing describes various embodiments of the
invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[0053] The proposed invention provides a system for detecting heart rate
condition of an individual which can be used in remotely.
[0054] The proposed invention provides a system for remotely detecting
heart rate condition of an individual which is cost effective and simple to use.
[0055] The proposed invention provides a system for detecting heart rate
condition of an individual which his developed to save rural life as they cannot
reach in time and it provide in very low cost.
[0056] The proposed invention provides a system for detecting heart rate
condition of an individual that can calculate the number of heart beats per minute
(bpm) and beat count based on obtained ECG signal form.

We Claim:
1. A system for detecting heart rate condition of an individual, the system
comprising:
a device configured to be worn by the individual, and the device is configured for sensing heart rate of the individual and correspondingly generate a first set of signals pertaining to the heart rate of the individual;
a processing unit operatively configured with the device, the processing unit comprising:
a memory associated with the processing unit for storing instructions which when executed cause the processing unit to: receive, from the device, the first set of signals; extract, from the first set of signals, time domain and amplitude domain features and correspondingly generate a second set of signals;
save, the first set of signals and the second set of signals in a training and testing database associated with the processing unit; and
validate, using deep learning technique, the time
domain and amplitude domain extracted feature and
correspondingly generating a third set of signals pertaining
to heart rate condition of the individual;
a mobile computing unit operatively configured to the device and
processing unit, wherein the mobile computing device is configured to
receive the third set of signals.
2. The system as claimed in claim 1, wherein the device comprises any or combination of monitoring electrodes with foam disposable backing, ECG sensor, and a communication unit, and wherein the communication unit is configured to communicate the first set if signals to the processing unit.
3. The system as claimed in claim 1, wherein the communication unit comprises any or combination of Bluetooth, and Wi-Fi.

4. The system as claimed in claim 1, wherein the time domain features comprises any or combination of QRS complex based wave feature, Pre-RR-interval, Post-RR-interval, Average RR-interval, and Average RR-interval.
5. The system as claimed in claim 1, wherein the amplitude domain features comprises any or combination of P wave, Q wave, R wave, S wave.
6. The system as claimed in claim 1, wherein the extraction of feature comprises:
Employing a plurality of high-pass filters of a first predefined frequency for time cut-off of low-pass frequency with static at a second pre-defined frequency; and
Calculating three conventional indexes for each of the plurality of high-pass filters such that a root mean square of the amplitude of any or combination of first set of signals, second set of signals and third set of signals is same as that of QRS complex.
7. The system as claimed in claim 6, wherein the plurality of high-pass filters
comprises bidirectional Butterworth filters.