Key result
An 8-layer 2D Convolutional Neural Network classified arrhythmia from ECG signals with an accuracy of 98.91% for 2 classes, 98.10% for 7 classes, and 98.45% for 8 classes using a 64x64 input size.
Why the study?
The authors sought to classify arrhythmia using a simpler 2D CNN image classifier with input sizes smaller than state-of-the-art models to determine the optimal input and accuracy.
A simpler 8-layer 2D CNN using smaller input sizes (64x64) can accurately classify up to 8 arrhythmia classes from ECG signals, reducing computational resource requirements while maintaining >98% accuracy.
CNN arrhythmia classification from ECG is hypothesis-generating; prospective clinical validation required before adoption.
Arrhythmia is an abnormal situation of heartbeat rate that may cause a critical condition to our body and this condition gets more dangerous as our cardiovascular system gets more vulnerable as we grow older. To diagnose this abnormality, the arrhythmia expert or cardiologist uses an electrocardiogram (ECG) by analyzing the pattern. ECG is a heartbeat signal that is produced by a tool called an electrocardiograph sensor that records the electrical impulses produced by the heart. Convolutional Neural Networks (CNN) is often used by researchers to classify ECG signals to Arryhtmia classes. The state-of-the-art research had applied CNN 2D (CNN 2D) with accuracy up to 99% with 128x128 image size obtained by transforming the ECG signal. In this paper, authors try to classify arrhythmia disorder with a different approach by creating simpler image classifier using CNN 2D with a smaller variety of input size that is smaller than state-the-art input and group the classes based on transformed ECG signal from MIT-BIH Arrhythmia database with the purpose to know what the most optimum input and the best accuracy to classify ECG signal image. The result of this research had produced an accuracy of up to 98.91% for 2 Classes, 98.10% for 7 Classes dan 98.45% for 8 Classes.
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Rohmantri et al. (2020) studied Arrhythmia (n=48). 8-layer 2D Convolutional Neural Network vs. Other input sizes and state-of-the-art models was evaluated on Classification accuracy. An 8-layer 2D Convolutional Neural Network classified arrhythmia from ECG signals with an accuracy of 98.91% for 2 classes, 98.10% for 7 classes, and 98.45% for 8 classes using a 64x64 input size.
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