ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG.

The electrocardiogram (ECG) is a standard test to monitor the activity of the heart. Many cardiac abnormalities are manifested in the ECG including arrhythmia that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal heart beats, and their correct classification based on ECG morphology. This paper proposes a novel and robust approach for representation learning of ECG sequences using a LSTM autoencoder for anomaly detection. The encoder part encodes the ECG signal into a lower dimensional latent space representation and decoder part then tries to reconstruct the specified ECG signal. The model is trained only on normal (non-anomalous) ECG signals. Then reconstruction loss of test ECG signals are calculated. Next a reconstruction loss threshold value is determined from the frequency distribution of the reconstruction losses so that from the reconstruction loss value above a certain threshold is determined as anomaly, otherwise it will be treated as normal. Determination of threshold is done using manual and Kapur's automated thresholding procedures. The aforementioned model has been applied on publicly available ECG5000 dataset. From the experimental results it is observed that the proposed model achieved more than 98% accuracy having precision, recall and F1 values more than 0.94, 0.97, 0.96 respectively. The performance of the proposed method is also found to be superior in most of the cases as compared to the results of seven other recent counter-part methods reported in the literature. [ABSTRACT FROM AUTHOR]