AU2021101272A4 - An artificial intelligence based heart rate monitoring system for sports training - Google Patents

An artificial intelligence based heart rate monitoring system for sports training Download PDF

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Publication number
AU2021101272A4
AU2021101272A4 AU2021101272A AU2021101272A AU2021101272A4 AU 2021101272 A4 AU2021101272 A4 AU 2021101272A4 AU 2021101272 A AU2021101272 A AU 2021101272A AU 2021101272 A AU2021101272 A AU 2021101272A AU 2021101272 A4 AU2021101272 A4 AU 2021101272A4
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AU
Australia
Prior art keywords
ecg
artificial intelligence
heart rate
monitoring system
sports training
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AU2021101272A
Inventor
Rajesh. C. B.
Nafih Cherappurath
Rajkumar E.
M. Elayaraja
G. S. Pradeep Ghantasala
Dilshith Azeezul Kabeer
Pravin R. Kshirsagar
Belsam Jeba Ananth M
Lateef Ahmad Mir
Anand Mohan
S. R. Mugunthan
P. Periyasamy
Vijayakumar Peroumal
K. G. Revathi
C. A. Sathiyamoorthy
Muhammed Ali T.
Syed Mufassir Yaseen
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Belsam Jeba Ananth M Dr
Cherappurath Nafih Dr
Elayaraja M Dr
Ghantasala GSPradeep Dr
Kabeer Dilshith Azeezul Dr
Mohan Anand Dr
Mugunthan S R Dr
Rajkumar E Dr
Revathi K G Dr
Sathiyamoorthy CA Dr
Original Assignee
Belsam Jeba Ananth M Dr
Cherappurath Nafih Dr
Elayaraja M Dr
Ghantasala G S Pradeep Dr
Kabeer Dilshith Azeezul Dr
Mohan Anand Dr
Mugunthan S R Dr
Rajkumar E Dr
Revathi K G Dr
Sathiyamoorthy C A Dr
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Application filed by Belsam Jeba Ananth M Dr, Cherappurath Nafih Dr, Elayaraja M Dr, Ghantasala G S Pradeep Dr, Kabeer Dilshith Azeezul Dr, Mohan Anand Dr, Mugunthan S R Dr, Rajkumar E Dr, Revathi K G Dr, Sathiyamoorthy C A Dr filed Critical Belsam Jeba Ananth M Dr
Priority to AU2021101272A priority Critical patent/AU2021101272A4/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/332—Portable devices specially adapted therefor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Abstract

AN ARTIFICIAL INTELLIGENCE BASED HEART RATE MONITORING SYSTEM FOR SPORTS TRAINING The present invention relates to an artificial intelligence based heart rate monitoring system for sports training The system involves front end hardware based on IoT that can be operated using smart application along with Al platform and cloud database for detection of cardiac rate. The proposed invention is able to detect heart rate. Herein an ECG patch consisting of wearable analog front end circuit with a Bluetooth module able to detect ECG signals. Real time ECG signal is displayed on the smart devices. ECG signals recorded from the wearable ECG patch is sent to cloud database where ECG signals of each of the user is stored, acting as a big data database for the Artificial Intelligence for detecting heart rate. 1/1 F I -~1 - -r .o.s...1 lbu Am L Figure 1

Description

1/1
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Figure 1
AN ARTIFICIAL INTELLIGENCE BASED HEART RATE MONITORING SYSTEM FOR SPORTS TRAINING
Field and background of the invention
Leading cause of heart disorder is Arrhythmia which is categorized into three types namely premature heart beat, tachycardia and bradycardia where most of the arrhythmias does not present any risk immediately and happens usually in our daily life. Acute stroke is mainly caused by atrial fibrillation and sudden shock or cardiac death is caused by mainly due to ventricular tachycardia. According to statistics taken from worldwide by World Health Organization, 15% of death has occurred due to arrhythmias and cardiovascular diseases are the reason for 80% of sudden deaths throughout the world.
Among the cardiovascular diseases, Arrhythmia is most important cause for death hence aged community people can be given continuous health care by utilizing wearable device for monitoring and detection of unusual electrocardiogram (ECG) signals such that instant warning message can be sent to hospital or concerned medical practitioner. Based on alert message immediate care is given to the patient avoiding happening of tragedies. In this invention several arrhythmias are focused for building an algorithm based on convolutional neural network (CNN) for the classification of cardiac disease. This health care platform of Artificial Intelligence (AI) involves IoT based wearable hardware, cloud database and user interface application.
Summary of Invention
In this invention Artificial Intelligence of Things (AloT) is proposed which focuses on real time analysis of ECG signals continuously for detection of any arrhythmias leading to risk to life. This system involves front end wearable device for sensing ECG, user interface application for smart devices, Al based algorithm for analysis of cardiac disease, cloud database for enabling real time detection, low consumption of power and longer duration of complete system usage. The sensing hardware device includes analog front end circuits consuming low power that includes integrated circuit for commercial power management along with a commercial Bluetooth module.
Hardware device is self designed system on chip (SOC) that includes sigma-delta 10 bit analog to digital converter, digital signal processing unit and a level shifter. Front end SOC collects ECG signal from the user which is transmitted to the smart device APP instantly via Bluetooth module that utilizes Bluetooth low energy 4.0. A single lead is provided with wearable ECG monitoring device that is attached with two wet electrodes of silver chloride to be fixed on the chest and it can be usable for 24 hours continuously under normal usage. Smart device APP acts as a user interface which involves mainly three parts name.
Brief description of the system
In this invention, cardiac disease detection is focused using AloT (Artificial Intelligence of Things) based on classification of ECG signals.
Cardiac arrhythmias are of four categories namely normal ECG, atrial fibrillation, atrial flutter and ventricular fibrillation.
Real time ECG signal will be displayed on screen which will be classified using convolutional neural network based Al algorithm into different types of arrhythmias stored in the cloud database.
We can also able to compute the classification on smart device itself where it will be able to classify whether the ECG signal is normal or abnormal but not the type of arrhythmia or the category where it falls.
Accurate classification will be done by cloud server which will be able to identify the type of arrhythmia precisely based on the ECG signal of the user obtained from the wearable wireless sensing device.
ECG signal will be stored in the local smart device transmitted through Bluetooth and also will be uploaded to cloud database.
Encoding of signal is done for safe transmission of ECG signal such that time stamps are added with it for maintenance of correctness.
Cloud server consist of big data database which involves three segments namely data storage, web user interface and Al algorithm for classification.
Data packets are received in encoded form by data storage segment from front end smart devices, converts data packets into decoded ECG signals.
Then these ECG signals will be stored separately in decoded form based on the measuring time stamps and measured objects.
Web user interface acts as a clear information platform for patients, their care takers and medical practitioners responsible for them. These stored ECG signals will enable doctors to diagnose condition of the patient more specifically while patient along with their family will be able to realize condition of the patient on daily basis based on ECG signal.
Thirdly, unusual ECG signals are detected by Al based algorithm from a huge set of data in few minutes using Convolutional neural network.
Generally one hundred thousand heartbeats will be produced by normal human per day where most of them are normal while few abnormal.This makes the doctor to diagnose, a great challenge from ECG data of long term hence our invention will be able to detect unusual signals quickly using cloud platform based on artificial intelligence based algorithm which is then displayed using web user interface.
Al based algorithm for classification of arrhythmia involves two segments namely data pre processing and Convolutional neural network model.
Better feature learning is provided by CNN model hence conventional signal processing of ECG signals such as feature extraction, R-peak, time
frequency analysis and QRS complex detection.
Pre-processing of data in Al algorithm involves three steps namely removal of noise, baseline removal and generation of image.
First step involves noise removal is done by 8-point moving average filter.
Moving average filter involves finite size windows using which convolution is taken along with the ECG signal obtained from the user.
Also average of output signal is taken in the filter for reducing the range of discrete time noise in turn enhances the peak value of identification.
8 point moving average filter is selected based on trial and error method.
Figure. Proposed Block diagram of Artificial Intelligence based Monitoring of Heart Disease
Description of the system
The concept of this invention is such that parametric curve is placed for approaching the location of known data set followed by subtraction of fitting parametric signal from the original signal for obtaining ECG data with removal of baseline drift. Image generation is done based on design that imitates how cardiologists are able to ECG signals. Whenever abnormal ECG occur sustained analysis and non - sustained analysis is done by cardiologist on ECG.
Cardiologist note unusual ECG signal whenever 3 abnormal heartbeats occurs consecutively which last more than 30 seconds.
Based on above concept, pre-processing of ECG signal is converted into image with a sample rate of 1200 Hz with time slot of 4.8 seconds.
These images will be used by CNN model for training process, validation and testing of data in the next stage where we implement one dimensional CNN for extraction of features and classification.
CNN model can also be realized using digital IC design. CNN model proposed in this invention involves four convolutional layers along with three fully connected layers.
Each of the convolutional layer is followed by a leaky rectified linear unit (leaky ReLU) acting as an active function.
These leaky ReLU have both benefits of traditional ReLU and also prevents several neurons from dead ReLU. Also partially input data is negative consisting of some vital features hence should not be ignored.
Hence due to these reasons leaky ReLU is selected as active function.
Max pooling for this model involves stride equal to 1 for extracting more precise features. Also same padding is taken for saving the edge information from each of the input image. Then three fully connected layers makes the output neurons count from 100 to 10 which is again shrinks these 10 neurons to 4 divisions as output.
Order of the filter is set as 10 at the first convolutional layer which is then doubled at each of the following layer.
Desired features are extracted by setting the order of all filter kernel to 180.
This model chooses its optimizer as gradient descent for optimization. Weight decay parameter, learning rate decay and learning rate of the model is set to 0.0001, 0.9 and 0.1 respectively after every epoch.

Claims (5)

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:
1. This invention focuses on platform of Artificial intelligence of Things integrated for health care system that includes hardware, software and storage cloud database.
2. Artificial Intelligence based algorithm is used for classification of arrhythmia based on professional advice of cardiologist.
3. ECG of user is sensor by the wearable device and transmitted to Smart device App using Bluetooth module.
4. IoT is utilized to transmit the sensed data from smart device to cloud database for classification process.
5. Al based algorithm will be able to classify arrhythmia into four categories namely normal ECG, atrial fibrillation, atrial flutter and ventricular fibrillation.
AU2021101272A 2021-03-11 2021-03-11 An artificial intelligence based heart rate monitoring system for sports training Ceased AU2021101272A4 (en)

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Application Number Priority Date Filing Date Title
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