Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling
| Autor: | Omar Abuzaghleh, Khaled M. Elleithy, Miad Faezipour, Muhammad Wasimuddin, Abdelshakour Abuzneid |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
ECG Signal Analysis
genetic structures Computer Networks and Communications Computer science 0206 medical engineering Wearable computer lcsh:TK7800-8360 02 engineering and technology Machine learning computer.software_genre Convolutional neural network ST Detection ECG Classification 0202 electrical engineering electronic engineering information engineering medicine cardiovascular diseases Myocardial infarction Electrical and Electronic Engineering Wearable technology business.industry lcsh:Electronics Cardiac arrhythmia medicine.disease 020601 biomedical engineering eye diseases Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Artificial intelligence Ecg signal business computer CNN Cardiac Arrhythmia |
| Zdroj: | Electronics, Vol 10, Iss 170, p 170 (2021) Electronics Volume 10 Issue 2 |
| ISSN: | 2079-9292 |
| Popis: | Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as &ldquo heart attack&rdquo is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user&rsquo s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|