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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Autores principales:	Jun Zhong, Dong Hai, Jiaxin Cheng, Changzhe Jiao, Shuiping Gou, Yongfeng Liu, Hong Zhou, Wenliang Zhu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	autoencoding electrocardiogram Gaussian mixture clustering heart rate estimation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/4afda4a83b3a4b41b7586247173ede0e
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spelling	oai:doaj.org-article:4afda4a83b3a4b41b7586247173ede0e2021-11-11T19:09:22ZConvolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors10.3390/s212171631424-8220https://doaj.org/article/4afda4a83b3a4b41b7586247173ede0e2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7163https://doaj.org/toc/1424-8220Heart 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.Jun ZhongDong HaiJiaxin ChengChangzhe JiaoShuiping GouYongfeng LiuHong ZhouWenliang ZhuMDPI AGarticleautoencodingelectrocardiogramGaussian mixture clusteringheart rate estimationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7163, p 7163 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	autoencoding electrocardiogram Gaussian mixture clustering heart rate estimation Chemical technology TP1-1185
spellingShingle	autoencoding electrocardiogram Gaussian mixture clustering heart rate estimation Chemical technology TP1-1185 Jun Zhong Dong Hai Jiaxin Cheng Changzhe Jiao Shuiping Gou Yongfeng Liu Hong Zhou Wenliang Zhu Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
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.
format	article
author	Jun Zhong Dong Hai Jiaxin Cheng Changzhe Jiao Shuiping Gou Yongfeng Liu Hong Zhou Wenliang Zhu
author_facet	Jun Zhong Dong Hai Jiaxin Cheng Changzhe Jiao Shuiping Gou Yongfeng Liu Hong Zhou Wenliang Zhu
author_sort	Jun Zhong
title	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_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_sort	convolutional autoencoding and gaussian mixture clustering for unsupervised beat-to-beat heart rate estimation of electrocardiograms from wearable sensors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4afda4a83b3a4b41b7586247173ede0e
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Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

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