ECG heartbeats classification with dilated convolutional autoencoder

Electrocardiography is essential for the early diagnosis and treatment of heart diseases, as undiagnosed heart diseases can lead to unfortunate outcomes such as patient loss. Autoencoder-based models have been used in the literature for ECG heartbeat classification. However, these models usually use the autoencoder in the feature extraction stage. The features obtained from the previous step are passed through a classifier for training. This indicates that the training procedure occurs in two phases. In this study, we performed autoencoder and classifier training simultaneously. This way, the network learned to minimize the overall loss while correctly reconstructing the input and extracting relevant features from the input data that are useful for the classification task. Such an approach has yet to be seen in the literature for ECG detection. The classification of six heartbeats (normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, atrial premature beat, and paced beat) obtained from the MIT-BIH dataset was performed using a convolutional autoencoder with an integrated classifier. The classification accuracy obtained in the test was found to be 99.99%.

Automated summary

Electrocardiography is crucial for early diagnosis and treatment of heart diseases. This study proposes a method that simultaneously trains an autoencoder and a classifier for ECG heartbeat classification. Testing on the MIT-BIH dataset demonstrates that this approach achieves a classification accuracy of 99.99%.