Classification of ECG signals by dot Residual LSTM Network with data augmentation for anomaly detection

Classification of ECG signals is of great importance for the detection of cardiac dysfunction. Recurrent Neural Network family has been greatly successful for time series related problems. In this paper, we compare different RNN variants and propose dot Residual LSTM network for ECG classification. Here, we use extracted features both from time and frequency domain with the network to improve the classification performance. A data generation scheme was developed with Conditional variational autoencoder (CVAE) and LSTM to increase training samples. A comparative analysis was studied to assess the performance of the model. The proposed dot Res LSTM achieved maximum accuracy of 80.00% and F1 score of 0.85. Furthermore, the model achieved maximum F1 score of 0.87 with augmented data. The study is expected to be useful in automatic cardiac diagnosis research.