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

被引:2
|
作者
Zhong, Jun [1 ,2 ]
Hai, Dong [1 ]
Cheng, Jiaxin [3 ]
Jiao, Changzhe [3 ]
Gou, Shuiping [3 ]
Liu, Yongfeng [1 ]
Zhou, Hong [1 ]
Zhu, Wenliang [1 ]
机构
[1] Chinese Acad Sci, Suzhou Inst Biomed Engn & Technol, Suzhou 215163, Peoples R China
[2] Univ Sci & Technol China, Suzhou Inst Biomed Engn & Technol, Hefei 230026, Peoples R China
[3] Xidian Univ, Sch Artificial Intelligence, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China
关键词
autoencoding; electrocardiogram; Gaussian mixture clustering; heart rate estimation; ALGORITHM; SYSTEM;
D O I
10.3390/s21217163
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:18
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