Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Purpose Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report. Design/methodology/approach The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection. Findings In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches. Originality/value In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

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Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Electronics ◽

10.3390/electronics10020170 ◽

2021 ◽

Vol 10 (2) ◽

pp. 170

Author(s):

Muhammad Wasimuddin ◽

Khaled Elleithy ◽

Abdelshakour Abuzneid ◽

Miad Faezipour ◽

Omar Abuzaghleh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Real Time ◽

Signal Analysis ◽

Neural Network Modeling ◽

Ecg Signal ◽

Analysis Model ◽

Learning Methods ◽

Machine Learning Methods

Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, 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’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.

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