MED-NET: a novel approach to ECG anomaly detection using LSTM auto-encoders
Koustav Dutta, Rasmita Lenka, Soumya Ranjan Nayak, Asimananda Khandual, Akash Kumar Bhoi
Abstract
Time series data is generated in various sectors of day to day life. Among all, one of the most important areas of generation and processing of time series data plays vital role in medical analysis. In this context, various continuous time series dependent EEG and ECG (electrocardiogram) signals are the most important types of medical signals produced and monitored by doctors. This paper proposes a highly novel and robust approach to analyse and detect ECG signals for tracking of anomalies in the signals using Hybrid Deep Learning Architectures (HDLA). The proposed scheme implements self-supervised pattern recognition using Long Short-Term Memory (LSTM) networks in terms of autoencoder and decoder. Finally, the proposed scheme is tested on Physio-net data set. The outcome of the model can also handle noise associated with ECG time series signal, it achieves high accuracy and also solves overfitting problems in a robust and efficient manner.