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Detecting Noisy ECG QRS Complexes Using WaveletCNN Autoencoder and ConvLSTM

Detecting Noisy ECG QRS Complexes Using WaveletCNN Autoencoder and ConvLSTM

Authors :
Brosnan Yuen
Xiaodai Dong
Tao Lu
Source :
IEEE Access, Vol 8, Pp 143802-143817 (2020)
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

In this paper, we propose a novel machine learning pipeline to detect QRS complexes in very noisy wearable electrocardiogram (ECG) devices. The machine learning pipeline consists of a Butterworth filter, two wavelet convolutional neural networks (WaveletCNNs) autoencoders, an optional QRS complex inverter, a Monte Carlo k-nearest neighbours (k-NN), and a convolutional long short-term memory (ConvLSTM). WaveletCNN autoencoders filter out electrode contact noise, instrumentation noise, and motion artifact noise by using the advantages of wavelet filters and convolutional neural networks. The QRS complex inverter flips inverted QRS complexes. Monte Carlo k-NN performs automatic gain control on the ECG signals in order to normalize it. The ConvLSTM executes the final QRS complex detection by using the power of a convolutional neural network and a long short-term memory. The MIT-BIH, the European ST-T, and the Long Term ST database Noise Stress Test databases provide the training and testing ECG recordings. The proposed machine learning pipeline performs 3 standard deviations better than the state of the art QRS complex detection algorithms in terms of F1 score for very noisy environments.

Details

Language :
English
ISSN :
21693536
Volume :
8
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.b34577921bff4b958323a12902694134
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2020.3012904