Analysis of physiological time series using wavelet transforms

A method of time-series analysis is presented using wavelet transforms to analyzed heart dynamics in sleep apneic patient. The difficulty in applying this analysis is that the phenomenon is nonstationary and contaminated with noise. The discrete wavelet transform detects signal changes by observing...

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Autores principales:	Figliola, María Alejandra, Serrano, Eduardo Pedro
Publicado:	1997
Materias:	Information cost function (ICF) Lyapunov exponent Physiological time series Sleep apnea Cardiovascular system Correlation methods Data recording Data reduction Electrocardiography Mathematical models Signal processing Sleep research Spectrum analysis Time series analysis Wavelet transforms Physiology adult article blood oxygen tension case report fourier transformation heart rhythm human male sleep apnea syndrome thorax time perception volumetry waveform Algorithms Cheyne-Stokes Respiration Computer Simulation Electroencephalography Forecasting Fourier Analysis Heart Rate Humans Lung Compliance Male Middle Aged Models, Cardiovascular Oximetry Oxygen Pulmonary Ventilation Signal Processing, Computer-Assisted Sleep Apnea Syndromes
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_07395175_v16_n3_p74_Figliola http://hdl.handle.net/20.500.12110/paper_07395175_v16_n3_p74_Figliola
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_07395175_v16_n3_p74_Figliola
record_format	dspace
spelling	paper:paper_07395175_v16_n3_p74_Figliola2023-06-08T15:44:25Z Analysis of physiological time series using wavelet transforms Figliola, María Alejandra Serrano, Eduardo Pedro Information cost function (ICF) Lyapunov exponent Physiological time series Sleep apnea Cardiovascular system Correlation methods Data recording Data reduction Electrocardiography Mathematical models Signal processing Sleep research Spectrum analysis Time series analysis Wavelet transforms Physiology adult article blood oxygen tension case report fourier transformation heart rhythm human male sleep apnea syndrome thorax time perception volumetry waveform Algorithms Cheyne-Stokes Respiration Computer Simulation Electrocardiography Electroencephalography Forecasting Fourier Analysis Heart Rate Humans Lung Compliance Male Middle Aged Models, Cardiovascular Oximetry Oxygen Pulmonary Ventilation Signal Processing, Computer-Assisted Sleep Apnea Syndromes A method of time-series analysis is presented using wavelet transforms to analyzed heart dynamics in sleep apneic patient. The difficulty in applying this analysis is that the phenomenon is nonstationary and contaminated with noise. The discrete wavelet transform detects signal changes by observing changes in the energy spectrum of the series. In all pre-apnea states, the dynamic system has higher information cost function (ICF) values than in the apnea condition. The system has lower ICF coefficients when the apnea crisis appears. Fil:Figliola, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Serrano, E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 1997 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_07395175_v16_n3_p74_Figliola http://hdl.handle.net/20.500.12110/paper_07395175_v16_n3_p74_Figliola
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Information cost function (ICF) Lyapunov exponent Physiological time series Sleep apnea Cardiovascular system Correlation methods Data recording Data reduction Electrocardiography Mathematical models Signal processing Sleep research Spectrum analysis Time series analysis Wavelet transforms Physiology adult article blood oxygen tension case report fourier transformation heart rhythm human male sleep apnea syndrome thorax time perception volumetry waveform Algorithms Cheyne-Stokes Respiration Computer Simulation Electrocardiography Electroencephalography Forecasting Fourier Analysis Heart Rate Humans Lung Compliance Male Middle Aged Models, Cardiovascular Oximetry Oxygen Pulmonary Ventilation Signal Processing, Computer-Assisted Sleep Apnea Syndromes
spellingShingle	Information cost function (ICF) Lyapunov exponent Physiological time series Sleep apnea Cardiovascular system Correlation methods Data recording Data reduction Electrocardiography Mathematical models Signal processing Sleep research Spectrum analysis Time series analysis Wavelet transforms Physiology adult article blood oxygen tension case report fourier transformation heart rhythm human male sleep apnea syndrome thorax time perception volumetry waveform Algorithms Cheyne-Stokes Respiration Computer Simulation Electrocardiography Electroencephalography Forecasting Fourier Analysis Heart Rate Humans Lung Compliance Male Middle Aged Models, Cardiovascular Oximetry Oxygen Pulmonary Ventilation Signal Processing, Computer-Assisted Sleep Apnea Syndromes Figliola, María Alejandra Serrano, Eduardo Pedro Analysis of physiological time series using wavelet transforms
topic_facet	Information cost function (ICF) Lyapunov exponent Physiological time series Sleep apnea Cardiovascular system Correlation methods Data recording Data reduction Electrocardiography Mathematical models Signal processing Sleep research Spectrum analysis Time series analysis Wavelet transforms Physiology adult article blood oxygen tension case report fourier transformation heart rhythm human male sleep apnea syndrome thorax time perception volumetry waveform Algorithms Cheyne-Stokes Respiration Computer Simulation Electrocardiography Electroencephalography Forecasting Fourier Analysis Heart Rate Humans Lung Compliance Male Middle Aged Models, Cardiovascular Oximetry Oxygen Pulmonary Ventilation Signal Processing, Computer-Assisted Sleep Apnea Syndromes
description	A method of time-series analysis is presented using wavelet transforms to analyzed heart dynamics in sleep apneic patient. The difficulty in applying this analysis is that the phenomenon is nonstationary and contaminated with noise. The discrete wavelet transform detects signal changes by observing changes in the energy spectrum of the series. In all pre-apnea states, the dynamic system has higher information cost function (ICF) values than in the apnea condition. The system has lower ICF coefficients when the apnea crisis appears.
author	Figliola, María Alejandra Serrano, Eduardo Pedro
author_facet	Figliola, María Alejandra Serrano, Eduardo Pedro
author_sort	Figliola, María Alejandra
title	Analysis of physiological time series using wavelet transforms
title_short	Analysis of physiological time series using wavelet transforms
title_full	Analysis of physiological time series using wavelet transforms
title_fullStr	Analysis of physiological time series using wavelet transforms
title_full_unstemmed	Analysis of physiological time series using wavelet transforms
title_sort	analysis of physiological time series using wavelet transforms
publishDate	1997
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_07395175_v16_n3_p74_Figliola http://hdl.handle.net/20.500.12110/paper_07395175_v16_n3_p74_Figliola
work_keys_str_mv	AT figliolamariaalejandra analysisofphysiologicaltimeseriesusingwavelettransforms AT serranoeduardopedro analysisofphysiologicaltimeseriesusingwavelettransforms
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Analysis of physiological time series using wavelet transforms

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