Pulse Signal Analysis Based on Deep Learning Network.
Pulse signal is one of the most important physiological features of human body, which is caused by the cyclical contraction and diastole. It has great research value and broad application prospect in the detection of physiological parameters, the development of medical equipment, and the study of ca...
| Published in: | BioMed Research International pp. 1 - 12 |
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| Format: | equations & formulas pictorial research tables/charts Journal Article |
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Wiley-Blackwell
9/15/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159141064&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159141064 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/15/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159141064 159141064 159141064 10.1155/2022/6256126 159141064 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Pulse Signal Analysis Based on Deep Learning Network. aug: au: E, Quanyu affil: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077 Hong Kong, China sug: subj: Pulse Signal Processing, Computer Assisted Evaluation Deep Learning Methods Neural Networks (Computer) Human Cardiovascular Diseases Wearable Sensors Noise Perception ab: Pulse signal is one of the most important physiological features of human body, which is caused by the cyclical contraction and diastole. It has great research value and broad application prospect in the detection of physiological parameters, the development of medical equipment, and the study of cardiovascular diseases and pulse diagnosis objective. In recent years, with the development of the sensor, measuring and saving of pulse signal has become very convenient. Now the pulse signal feature analysis is a hotspot and difficulty in the signal processing field. Therefore, to realize pulse signal automatic analysis and recognition is vital significance in the aspects of the noninvasive diagnosis and remote monitoring, etc. In this article, we combined the pulse signal feature extraction in time and frequency domain and convolution neural network to analyze the pulse signal. Firstly, a theory of wavelet transform and the ensemble empirical mode decomposition (EEMD) which is gradually developed in recent years have been used to remove the noises in the pulse signal. Moreover, a method of feature point detection based on differential threshold method is proposed which realized the accurate positioning and extraction time-domain values. Finally, a deep learning method based on one-dimensional CNN has been utilized to make the classification of multiple pulse signals in the article. In conclusion, a deep learning method is proposed for the pulse signal classification combined with the feature extraction in time and frequency domain in this article. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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