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ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG by Moumita Roy; Sukanta Majumder; Anindya Halder; Utpal Biswas is a Computer Science article available to read on EtoBox.

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## A C T 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 availab

Who reads ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG?

Author

Moumita Roy; Sukanta Majumder; Anindya Halder; Utpal Biswas

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)