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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Autores principales: Risk, M.R., Slezak, D.F., Turjanski, P., Panelli, A., Taborda, R.A.M., Marshall, G.
Formato: CONF
Materias:
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
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_02766574_v34_n_p541_Risk
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_02766574_v34_n_p541_Risk
record_format dspace
spelling todo:paper_02766574_v34_n_p541_Risk2023-10-03T15:15:58Z Time series calculation of heart rate using multi rate FIR filters Risk, M.R. Slezak, D.F. Turjanski, P. Panelli, A. Taborda, R.A.M. Marshall, G. 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. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Risk, M.R.
Slezak, D.F.
Turjanski, P.
Panelli, A.
Taborda, R.A.M.
Marshall, G.
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.
format CONF
author Risk, M.R.
Slezak, D.F.
Turjanski, P.
Panelli, A.
Taborda, R.A.M.
Marshall, G.
author_facet Risk, M.R.
Slezak, D.F.
Turjanski, P.
Panelli, A.
Taborda, R.A.M.
Marshall, G.
author_sort Risk, M.R.
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
url http://hdl.handle.net/20.500.12110/paper_02766574_v34_n_p541_Risk
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AT slezakdf timeseriescalculationofheartrateusingmultiratefirfilters
AT turjanskip timeseriescalculationofheartrateusingmultiratefirfilters
AT panellia timeseriescalculationofheartrateusingmultiratefirfilters
AT tabordaram timeseriescalculationofheartrateusingmultiratefirfilters
AT marshallg timeseriescalculationofheartrateusingmultiratefirfilters
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