Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Q: What is the clinical evidence from this study?

Study design: Other. Population: Arrhythmia. Intervention: Automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) vs. Existing methods. Primary outcome: Overall performance (positive predictive value, sensitivity, F1-score, and accuracy) (F1-score increased by 4.75~16.85%).

Key Result

An automatic ECG classification method using CWT and CNN achieved an overall accuracy of 98.74% and increased the overall F1-score by 4.75-16.85% compared with existing methods.

PICO

Population

Arrhythmia

Intervention / Comparator

Automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) vs Existing methods

Primary Outcome

Overall performance (positive predictive value, sensitivity, F1-score, and accuracy) — F1-score increased by 4.75~16.85%

Main Result

Effect estimate: F1-score increased by 4.75~16.85%

Abstract

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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Cite This Study

Wang et al. (Mon,) conducted a other in Arrhythmia. Automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) vs. Existing methods was evaluated on Overall performance (positive predictive value, sensitivity, F1-score, and accuracy) (F1-score increased by 4.75~16.85%). An automatic ECG classification method using CWT and CNN achieved an overall accuracy of 98.74% and increased the overall F1-score by 4.75-16.85% compared with existing methods.

synapsesocial.com/papers/6a204b037a3568d6afd1e389 https://doi.org/https://doi.org/10.3390/e23010119

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