Review paper

ECG anomaly class identification using LSTM and error profile modeling

Volume: 109, Pages: 14 - 21
Published: Jun 1, 2019
Abstract
Automatic diagnosis of cardiac events is a current problem of interest in which deep learning has shown promising success. We have earlier reported the use of Long Short Term Memory (LSTM) networks-trained on normal ECG patterns-to the detection of anomalies from the prediction errors for real-time diagnostic applications. In this work, we extend our anomaly detection algorithm by introducing a second stage predictor that can identify the actual...
Paper Details
Title
ECG anomaly class identification using LSTM and error profile modeling
Published Date
Jun 1, 2019
Volume
109
Pages
14 - 21
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