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Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian...

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Detalles Bibliográficos
Autores principales: Zhong, Jun, Hai, Dong, Cheng, Jiaxin, Jiao, Changzhe, Gou, Shuiping, Liu, Yongfeng, Zhou, Hong, Zhu, Wenliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587957/
https://www.ncbi.nlm.nih.gov/pubmed/34770469
http://dx.doi.org/10.3390/s21217163
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author Zhong, Jun
Hai, Dong
Cheng, Jiaxin
Jiao, Changzhe
Gou, Shuiping
Liu, Yongfeng
Zhou, Hong
Zhu, Wenliang
author_facet Zhong, Jun
Hai, Dong
Cheng, Jiaxin
Jiao, Changzhe
Gou, Shuiping
Liu, Yongfeng
Zhou, Hong
Zhu, Wenliang
author_sort Zhong, Jun
collection PubMed
description Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.
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spelling pubmed-85879572021-11-13 Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors Zhong, Jun Hai, Dong Cheng, Jiaxin Jiao, Changzhe Gou, Shuiping Liu, Yongfeng Zhou, Hong Zhu, Wenliang Sensors (Basel) Article Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices. MDPI 2021-10-28 /pmc/articles/PMC8587957/ /pubmed/34770469 http://dx.doi.org/10.3390/s21217163 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhong, Jun
Hai, Dong
Cheng, Jiaxin
Jiao, Changzhe
Gou, Shuiping
Liu, Yongfeng
Zhou, Hong
Zhu, Wenliang
Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
title Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
title_full Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
title_fullStr Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
title_full_unstemmed Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
title_short Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
title_sort convolutional autoencoding and gaussian mixture clustering for unsupervised beat-to-beat heart rate estimation of electrocardiograms from wearable sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587957/
https://www.ncbi.nlm.nih.gov/pubmed/34770469
http://dx.doi.org/10.3390/s21217163
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