Time series calculation of heart rate using multi rate FIR filters
The spectral analysis of heart rate variability, based on the Fourier transform, needs even sampled data. The objectives of this study were to develop an interpolation method based on multi rate FIR filters, and then to implement this method for parallel processing machines. A total ofthree data set...
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02766574_v34_n_p541_Risk http://hdl.handle.net/20.500.12110/paper_02766574_v34_n_p541_Risk |
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paper:paper_02766574_v34_n_p541_Risk2023-06-08T15:25:54Z Time series calculation of heart rate using multi rate FIR filters Turjanski, Pablo Guillermo Bland and altman analysis Cubic splines Data sets Heart rate variabilities Heart rates High frequency bands Interpolation methods Long terms Low frequency bands Multi rates Parallel processing Sampled datum Spectral analysis Cardiology Fourier transforms Frequency bands Interpolation Parallel programming Spectrum analysis Spectrum analyzers Time series FIR filters The spectral analysis of heart rate variability, based on the Fourier transform, needs even sampled data. The objectives of this study were to develop an interpolation method based on multi rate FIR filters, and then to implement this method for parallel processing machines. A total ofthree data sets were used: a) simulated heart rate with an IPFM model, b) autonomic blockage database (both pharmacological and postural), and c) long term Holter studies (recordings of 24 hours). Spectral analysis, for the three data sets, was processed for both interpolation using FIR filters and cubic splines, the results for Bland and Altman analysis for low frequency band, showed a difference[ of-47±131 ms2; then for the high frequency band, the difference was 3±48 ms2. The presented method of time series calculation, using FIR filters, probed to be equivalent for both simulated and real data, and is suitable for parallel programming implementation. Fil:Turjanski, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02766574_v34_n_p541_Risk http://hdl.handle.net/20.500.12110/paper_02766574_v34_n_p541_Risk |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Bland and altman analysis Cubic splines Data sets Heart rate variabilities Heart rates High frequency bands Interpolation methods Long terms Low frequency bands Multi rates Parallel processing Sampled datum Spectral analysis Cardiology Fourier transforms Frequency bands Interpolation Parallel programming Spectrum analysis Spectrum analyzers Time series FIR filters |
spellingShingle |
Bland and altman analysis Cubic splines Data sets Heart rate variabilities Heart rates High frequency bands Interpolation methods Long terms Low frequency bands Multi rates Parallel processing Sampled datum Spectral analysis Cardiology Fourier transforms Frequency bands Interpolation Parallel programming Spectrum analysis Spectrum analyzers Time series FIR filters Turjanski, Pablo Guillermo Time series calculation of heart rate using multi rate FIR filters |
topic_facet |
Bland and altman analysis Cubic splines Data sets Heart rate variabilities Heart rates High frequency bands Interpolation methods Long terms Low frequency bands Multi rates Parallel processing Sampled datum Spectral analysis Cardiology Fourier transforms Frequency bands Interpolation Parallel programming Spectrum analysis Spectrum analyzers Time series FIR filters |
description |
The spectral analysis of heart rate variability, based on the Fourier transform, needs even sampled data. The objectives of this study were to develop an interpolation method based on multi rate FIR filters, and then to implement this method for parallel processing machines. A total ofthree data sets were used: a) simulated heart rate with an IPFM model, b) autonomic blockage database (both pharmacological and postural), and c) long term Holter studies (recordings of 24 hours). Spectral analysis, for the three data sets, was processed for both interpolation using FIR filters and cubic splines, the results for Bland and Altman analysis for low frequency band, showed a difference[ of-47±131 ms2; then for the high frequency band, the difference was 3±48 ms2. The presented method of time series calculation, using FIR filters, probed to be equivalent for both simulated and real data, and is suitable for parallel programming implementation. |
author |
Turjanski, Pablo Guillermo |
author_facet |
Turjanski, Pablo Guillermo |
author_sort |
Turjanski, Pablo Guillermo |
title |
Time series calculation of heart rate using multi rate FIR filters |
title_short |
Time series calculation of heart rate using multi rate FIR filters |
title_full |
Time series calculation of heart rate using multi rate FIR filters |
title_fullStr |
Time series calculation of heart rate using multi rate FIR filters |
title_full_unstemmed |
Time series calculation of heart rate using multi rate FIR filters |
title_sort |
time series calculation of heart rate using multi rate fir filters |
publishDate |
2007 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02766574_v34_n_p541_Risk http://hdl.handle.net/20.500.12110/paper_02766574_v34_n_p541_Risk |
work_keys_str_mv |
AT turjanskipabloguillermo timeseriescalculationofheartrateusingmultiratefirfilters |
_version_ |
1768544364702728192 |