Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network

Electrocardiogram (ECG) technology plays a vital role in detecting arrhythmia. Numerous achievements have been marked in ECG-related research. Most methods first pre-process ECG signals, then extract features, and finally classify them. Most of the ECG signals used in the related studies were analyzed in specific time intervals or using a fixed number of samples. However, it is not always possible to see significant changes in a short term, and the symptoms of some patients are relatively short-lived. Misjudgments are possible because the ECG signal was not accurately extracted. This study proposes a computer-aided diagnosis (CAD) system for classification of Atrial Fibrillation and Normal Sinus Rhythm based on ECG signals through convolutional neural network. The proposed system considers a single heartbeat, rather than a specific number of seconds. This study eschews the one-dimensional digital ECG signal used in previous studies and uses convolutional neural networks to analyze two-dimensional ECG image. This study explores whether two-dimensional image ECG requires signal filtering. The final classification results in filtered ECG signals is accuracy of 99.23%, sensitivity of 99.71%, and specificity of 98.66%. The best result in non-filtered ECG signals achieves accuracy of 99.18%, sensitivity of 99.31%, and specificity of 99.03%. With no cumbersome artificial settings, the results of this study are comparable to the related studies. The proposed CAD system has high generalizability; it can help doctors to diagnose diseases effectively and reduce misdiagnosis.