Key result
A 2D Recurrence Plot-based deep learning approach using Inception-ResNet-v2 achieved an average F1-score of 0.844 for classifying nine types of cardiac arrhythmias using only two ECG leads.
Why the study?
Current time-domain deep-learning methods for cardiac arrhythmia classification from ECG signals are not sufficiently robust or satisfactory.
A novel deep learning approach converting 1D ECG signals into 2D recurrence plot images demonstrates high accuracy for automated cardiac arrhythmia classification using only two leads.
May aid two-lead arrhythmia detection; hypothesis-generating and requires prospective validation before clinical use.
The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods' performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.
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Zhang et al. (2021) studied Cardiac Arrhythmia (n=6,877). Recurrence Plot-based approach using Inception-ResNet-v2 vs. 1D ECG-based methods and other 2D image transforms was evaluated on Average F1-score for 9-class cardiac arrhythmia classification. A 2D Recurrence Plot-based deep learning approach using Inception-ResNet-v2 achieved an average F1-score of 0.844 for classifying nine types of cardiac arrhythmias using only two ECG leads.
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