ECG signal enhancement based on improved denoising auto-encoder
Xiong, Peng1; Wang, Hongrui1,2; Liu, Ming2; Zhou, Suiping3; Hou, Zengguang4; Liu, Xiuling2
2016-06-01
发表期刊ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷号52页码:194-202
文章类型Article
摘要The electrocardiogram (ECG) is a primary diagnostic tool for examining cardiac tissue and structures. ECG signals are often contaminated by noise, which can manifest with similar morphologies as an ECG waveform in, the frequency domain. In this paper, a novel deep neural network (DNN) is proposed to solve the above mentioned problem. This DNN is created from an improved denoising auto-encoder (DAE) reformed by a wavelet transform (WT), method. A WT with scale-adaptive thresholding method is used to filter most of the noise. A DNN based on improved DAE is then used to remove any residual noise, which is often complex with an unknown distribution in the frequency domain. The proposed method was evaluated on ECG signals from the MIT-BIH Arrhythmia database, and added noise signals were obtained from the MIT-BIH Noise Stress Test database. The results show that the,average of output signal-to-noise ratio (SNR) is from 21.56 dB to 22.96 dB, and the average of root mean square error (RMSE) is less than 0.037. The proposed method showed significant improvement in SNR and RMSE compared with the individual processing with either a WT or DAE, thus providing promising approaches for ECG signal enhancement (C) 2016 Elsevier Ltd. All rights reserved.
关键词Denoising Auto-encoder (Dae) Ecg Signal Denoising Wavelet Transform (Wt) Deep Neural Network (Dnn)
WOS标题词Science & Technology ; Technology
DOI10.1016/j.engappai.2016.02.015
关键词[WOS]ADAPTIVE KALMAN FILTER ; NEURAL-NETWORKS
收录类别SCI
语种英语
项目资助者Natural Science Foundation of Hebei Province(F2015201112) ; Funds for Distinguished Young Scientists of Hebei Province(F2016201186) ; Colleges and Universities in Hebei Province Science and Technology Research Point Project(ZD2015067)
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
WOS类目Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号WOS:000379631100018
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12156
专题复杂系统管理与控制国家重点实验室_先进机器人
作者单位1.Yanshan Univ, Coll Elect & Informat Engn, Qinhuangdao, Peoples R China
2.Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding, Peoples R China
3.Middlesex Univ, Sch Sci & Technol, London N17 8HR, England
4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
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Xiong, Peng,Wang, Hongrui,Liu, Ming,et al. ECG signal enhancement based on improved denoising auto-encoder[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2016,52:194-202.
APA Xiong, Peng,Wang, Hongrui,Liu, Ming,Zhou, Suiping,Hou, Zengguang,&Liu, Xiuling.(2016).ECG signal enhancement based on improved denoising auto-encoder.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,52,194-202.
MLA Xiong, Peng,et al."ECG signal enhancement based on improved denoising auto-encoder".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 52(2016):194-202.
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