{"title":"基于昼夜节律的24小时HRV数据非线性特征提取","authors":"Kapil Tajane, Rahul Pitale, J. Umale","doi":"10.1109/I2CT.2014.7092205","DOIUrl":null,"url":null,"abstract":"From few decades ECG signal is used as a baseline to determine the hearts condition. It is very much essential to detect and process ECG signal accurately. Heart Rate Variability (HRV) is an effective mechanism to analyze the cardiac health of a patient. In this paper we proposed new technique to detect linear as well as non-linear characteristics of 24 hour HRV data based on circadian rhythm. A circadian rhythm is nothing but a approximately 24 hour clock present in living beings within the physiological process, which affects the HRV. The motivation of this paper is to define the set of rules for 24 hour HRV data, so that it will be easily applicable. As HRV is self similar, so we have to find the self similarity of 24 hour HRV data into 5-10 min of HRV data.","PeriodicalId":384966,"journal":{"name":"International Conference for Convergence for Technology-2014","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-linear feature extraction of 24-hours HRV data based on circadian rhythm\",\"authors\":\"Kapil Tajane, Rahul Pitale, J. Umale\",\"doi\":\"10.1109/I2CT.2014.7092205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"From few decades ECG signal is used as a baseline to determine the hearts condition. It is very much essential to detect and process ECG signal accurately. Heart Rate Variability (HRV) is an effective mechanism to analyze the cardiac health of a patient. In this paper we proposed new technique to detect linear as well as non-linear characteristics of 24 hour HRV data based on circadian rhythm. A circadian rhythm is nothing but a approximately 24 hour clock present in living beings within the physiological process, which affects the HRV. The motivation of this paper is to define the set of rules for 24 hour HRV data, so that it will be easily applicable. As HRV is self similar, so we have to find the self similarity of 24 hour HRV data into 5-10 min of HRV data.\",\"PeriodicalId\":384966,\"journal\":{\"name\":\"International Conference for Convergence for Technology-2014\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference for Convergence for Technology-2014\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2CT.2014.7092205\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference for Convergence for Technology-2014","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2CT.2014.7092205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-linear feature extraction of 24-hours HRV data based on circadian rhythm
From few decades ECG signal is used as a baseline to determine the hearts condition. It is very much essential to detect and process ECG signal accurately. Heart Rate Variability (HRV) is an effective mechanism to analyze the cardiac health of a patient. In this paper we proposed new technique to detect linear as well as non-linear characteristics of 24 hour HRV data based on circadian rhythm. A circadian rhythm is nothing but a approximately 24 hour clock present in living beings within the physiological process, which affects the HRV. The motivation of this paper is to define the set of rules for 24 hour HRV data, so that it will be easily applicable. As HRV is self similar, so we have to find the self similarity of 24 hour HRV data into 5-10 min of HRV data.