{"title":"基于快速高斯网格的心率变异性快速准确功率谱分析","authors":"Charalampos Eleftheriadis, G. Karakonstantis","doi":"10.23919/cinc53138.2021.9662927","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an algorithm for fast and accurate Power Spectral Analysis of Heart Rate Variability based on the Lomb Periodogram. The previously introduced Fast-Lomb periodogram, may have reduced the computational complexity of PSA, however it still requires a large oversampling factor, which increases the complexity of the needed FFTs. In our approach, by utilising the Fast Gaussian Gridding method we produce accurate evenly spaced grids for the required FFTs by restricting the oversampling factor only to 2. By doing so, the required FFT size is reduced by up to 4 times without compromising the output accuracy. Our results indicate that the proposed spectral analysis system can achieve upto 76.55% savings in the number of operations or up-to 75.8% in terms of the total execution time.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fast and Accurate Power Spectral Analysis of Heart Rate Variability using Fast Gaussian Gridding\",\"authors\":\"Charalampos Eleftheriadis, G. Karakonstantis\",\"doi\":\"10.23919/cinc53138.2021.9662927\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an algorithm for fast and accurate Power Spectral Analysis of Heart Rate Variability based on the Lomb Periodogram. The previously introduced Fast-Lomb periodogram, may have reduced the computational complexity of PSA, however it still requires a large oversampling factor, which increases the complexity of the needed FFTs. In our approach, by utilising the Fast Gaussian Gridding method we produce accurate evenly spaced grids for the required FFTs by restricting the oversampling factor only to 2. By doing so, the required FFT size is reduced by up to 4 times without compromising the output accuracy. Our results indicate that the proposed spectral analysis system can achieve upto 76.55% savings in the number of operations or up-to 75.8% in terms of the total execution time.\",\"PeriodicalId\":126746,\"journal\":{\"name\":\"2021 Computing in Cardiology (CinC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Computing in Cardiology (CinC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/cinc53138.2021.9662927\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Computing in Cardiology (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/cinc53138.2021.9662927","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast and Accurate Power Spectral Analysis of Heart Rate Variability using Fast Gaussian Gridding
In this paper, we propose an algorithm for fast and accurate Power Spectral Analysis of Heart Rate Variability based on the Lomb Periodogram. The previously introduced Fast-Lomb periodogram, may have reduced the computational complexity of PSA, however it still requires a large oversampling factor, which increases the complexity of the needed FFTs. In our approach, by utilising the Fast Gaussian Gridding method we produce accurate evenly spaced grids for the required FFTs by restricting the oversampling factor only to 2. By doing so, the required FFT size is reduced by up to 4 times without compromising the output accuracy. Our results indicate that the proposed spectral analysis system can achieve upto 76.55% savings in the number of operations or up-to 75.8% in terms of the total execution time.