January 14, 2021Open Access

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

Q: What is the clinical evidence from this study?

Study design: Other. Population: Cardiac arrhythmia. Intervention: Global Average-Based 2-D Convolutional Neural Network vs. Existing solutions. Primary outcome: Classification accuracy.

MWMuhammad WasimuddinUniversity of Bridgeport KEKhaled ElleithyUniversity of Bridgeport AAAbdelshakour AbuzneidUniversity of Bridgeport

Key Result

A global average-based 2-D Convolutional Neural Network model identified multiple arrhythmia types from single-lead ECG signals with an accuracy of 99.23%.

Structured PICO

Does a three-layer CNN-based classifier accurately identify multiple arrhythmia types from single-lead ECG signals?

Population

ECG signals from the European ST-T database recorded with single lead L3

Intervention

Three-layer Convolutional Neural Network (CNN)-based classifier model using two-dimensional images of ECG waves

Comparator

Existing solutions

Outcome

Accuracy of identifying multiple arrhythmia typessurrogate

A novel, less complex CNN-based classifier achieved 99.23% accuracy in detecting arrhythmias from single-lead ECGs, potentially enabling real-time monitoring on wearable devices.

Abstract

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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