Journal Article

A multilayer LSTM auto-encoder for fetal ECG anomaly detection

Abstract

The paper introduces a multilayer long short-term memory (LSTM) based auto-encoder network to spot abnormalities in fetal ECG. The LSTM network was used to detect patterns in the time series, reconstruct errors and classify a given segment as an anomaly or not. The proposed anomaly detection method provides a filtering procedure able to reproduce ECG variability based on the semi-supervised paradigm. Experiments show that the proposed method can learn better features than the traditional approach without any prior knowledge and subject to proper signal identification can facilitate the analysis of fetal ECG signals in daily life.

Attached files

Authors

Skarga-Bandurova, Inna
Biloborodova, Tetiana
Skarha-Bandurov, Illia
Boltov, Yehor
Derkach, Maryna

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2021
Date of RADAR deposit: 2022-07-01

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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