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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...

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Detalles Bibliográficos
Autor principal: E, Quanyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499787/
https://www.ncbi.nlm.nih.gov/pubmed/36158878
http://dx.doi.org/10.1155/2022/6256126
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author E, Quanyu
author_facet E, Quanyu
author_sort E, Quanyu
collection PubMed
description 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.
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spelling pubmed-94997872022-09-23 Pulse Signal Analysis Based on Deep Learning Network E, Quanyu Biomed Res Int Research Article 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. Hindawi 2022-09-15 /pmc/articles/PMC9499787/ /pubmed/36158878 http://dx.doi.org/10.1155/2022/6256126 Text en Copyright © 2022 Quanyu E. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
E, Quanyu
Pulse Signal Analysis Based on Deep Learning Network
title Pulse Signal Analysis Based on Deep Learning Network
title_full Pulse Signal Analysis Based on Deep Learning Network
title_fullStr Pulse Signal Analysis Based on Deep Learning Network
title_full_unstemmed Pulse Signal Analysis Based on Deep Learning Network
title_short Pulse Signal Analysis Based on Deep Learning Network
title_sort pulse signal analysis based on deep learning network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499787/
https://www.ncbi.nlm.nih.gov/pubmed/36158878
http://dx.doi.org/10.1155/2022/6256126
work_keys_str_mv AT equanyu pulsesignalanalysisbasedondeeplearningnetwork