基于支持向量机和多特征融合的光容积图信号质量评估

Jie Zhang, Licai Yang, Zhonghua Su, Xueqin Mao, Kan Luo, Chengyu Liu
{"title":"基于支持向量机和多特征融合的光容积图信号质量评估","authors":"Jie Zhang, Licai Yang, Zhonghua Su, Xueqin Mao, Kan Luo, Chengyu Liu","doi":"10.1166/JMIHI.2018.2530","DOIUrl":null,"url":null,"abstract":"Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal.\n Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each\n had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to\n classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66%\n were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Photoplethysmogram Signal Quality Assessment Using Support Vector Machine and Multi-Feature Fusion\",\"authors\":\"Jie Zhang, Licai Yang, Zhonghua Su, Xueqin Mao, Kan Luo, Chengyu Liu\",\"doi\":\"10.1166/JMIHI.2018.2530\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal.\\n Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each\\n had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to\\n classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66%\\n were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.\",\"PeriodicalId\":49032,\"journal\":{\"name\":\"Journal of Medical Imaging and Health Informatics\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Imaging and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1166/JMIHI.2018.2530\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1166/JMIHI.2018.2530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

背景:在生理信号测量系统中,噪声是不可避免的。质量差的信号会影响分析结果,使后续的临床诊断失效。因此,在我们解释信号之前,有必要进行信号质量评估。目的:本文提出了一种结合支持向量机(SVM)和多特征融合的脉冲波形信号质量评估方法,重点研究了光体积脉搏图(PPG)。方法:记录53名健康志愿者的PPG信号。每个都有5分钟的长度。每一次心跳的信号质量由临床专家手工标注,然后根据每一次心跳段的结果自动计算5 s发作的信号质量,共得到13294个5-s PPG段。然后,通过输入一组12个信号质量特征,训练支持向量机对干净/噪声PPG录音进行分类。进一步的实验验证了基于支持向量机的信号质量分类器方法。结果:10倍交叉验证的平均准确率为87.90%,灵敏度为88.10%,特异性为87.66%。结论:该方法可准确分类PPGs的信号质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Photoplethysmogram Signal Quality Assessment Using Support Vector Machine and Multi-Feature Fusion
Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal. Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66% were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Medical Imaging and Health Informatics
Journal of Medical Imaging and Health Informatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas. As an example, the Distributed Diagnosis and Home Healthcare (D2H2) aims to improve the quality of patient care and patient wellness by transforming the delivery of healthcare from a central, hospital-based system to one that is more distributed and home-based. Different medical imaging modalities used for extraction of information from MRI, CT, ultrasound, X-ray, thermal, molecular and fusion of its techniques is the focus of this journal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信