Key result
Novel CNN-wavelet model boosts ECG classification accuracy to ~99% over standard methods.
Why the study?
Manual beat-by-beat ECG evaluation for early arrhythmia detection is time-consuming and laborious in clinical practice.
Does a deep learning method using Continuous Wavelet Transform and Convolutional Neural Networks improve the accuracy of automatic ECG heartbeat classification compared to existing algorithms?
Does a deep learning method using Continuous Wavelet Transform and Convolutional Neural Networks improve the accuracy of automatic ECG heartbeat classification compared to existing algorithms?
The proposed deep learning framework demonstrates highly accurate automatic ECG classification, establishing a foundation for next-generation clinical auxiliary diagnostic tools for arrhythmias.
Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.
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Wang et al. (2021) studied Arrhythmia (n=47). Automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) vs. Existing automatic ECG classification methods was evaluated on Overall classification F1-score. An automatic ECG classification method using continuous wavelet transform and a convolutional neural network achieved 98.74% accuracy and increased the overall F1-score by 4.75% to 16.85% compared to existing methods.
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