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ECG signal enhancement based on improved denoising auto-encoder
Xiong, Peng1; Wang, Hongrui1,2; Liu, Ming2; Zhou, Suiping3; Hou, Zengguang4; Liu, Xiuling2
Source PublicationENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
2016-06-01
Volume52Pages:194-202
SubtypeArticle
AbstractThe 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.
KeywordDenoising Auto-encoder (Dae) Ecg Signal Denoising Wavelet Transform (Wt) Deep Neural Network (Dnn)
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.engappai.2016.02.015
WOS KeywordADAPTIVE KALMAN FILTER ; NEURAL-NETWORKS
Indexed BySCI
Language英语
Funding OrganizationNatural 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 Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS IDWOS:000379631100018
Citation statistics
Cited Times:25[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12156
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.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
Recommended Citation
GB/T 7714
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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