{"title":"基于二进小波的心电信号处理及其心理压力评估","authors":"G. Ranganathan, V. Bindhu, R. Rangarajan","doi":"10.1109/ICBBE.2010.5516360","DOIUrl":null,"url":null,"abstract":"This paper presents the evaluation of mental stress assessment using heart rate variability. The heart rate signals are processed first using Fourier transform, then it is applied to wavelet transform. The activity of the autonomic nervous system is noninvasively studied by means of autoregressive (AR) frequency analysis of the heart-rate variability (HRV) signal. Spectral decomposition of the Heart Rate Variability during whole night recordings was obtained, in order to assess the characteristic fluctuations in the heart rate. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1)online monitoring of heart rate signals, 2) signal processing using the Dyadic wavelet.","PeriodicalId":6396,"journal":{"name":"2010 4th International Conference on Bioinformatics and Biomedical Engineering","volume":"78 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"ECG Signal Processing Using Dyadic Wavelet for Mental Stress Assessment\",\"authors\":\"G. Ranganathan, V. Bindhu, R. Rangarajan\",\"doi\":\"10.1109/ICBBE.2010.5516360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the evaluation of mental stress assessment using heart rate variability. The heart rate signals are processed first using Fourier transform, then it is applied to wavelet transform. The activity of the autonomic nervous system is noninvasively studied by means of autoregressive (AR) frequency analysis of the heart-rate variability (HRV) signal. Spectral decomposition of the Heart Rate Variability during whole night recordings was obtained, in order to assess the characteristic fluctuations in the heart rate. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1)online monitoring of heart rate signals, 2) signal processing using the Dyadic wavelet.\",\"PeriodicalId\":6396,\"journal\":{\"name\":\"2010 4th International Conference on Bioinformatics and Biomedical Engineering\",\"volume\":\"78 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 4th International Conference on Bioinformatics and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBBE.2010.5516360\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 4th International Conference on Bioinformatics and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBBE.2010.5516360","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ECG Signal Processing Using Dyadic Wavelet for Mental Stress Assessment
This paper presents the evaluation of mental stress assessment using heart rate variability. The heart rate signals are processed first using Fourier transform, then it is applied to wavelet transform. The activity of the autonomic nervous system is noninvasively studied by means of autoregressive (AR) frequency analysis of the heart-rate variability (HRV) signal. Spectral decomposition of the Heart Rate Variability during whole night recordings was obtained, in order to assess the characteristic fluctuations in the heart rate. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1)online monitoring of heart rate signals, 2) signal processing using the Dyadic wavelet.
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