New portable and wearable devices as well as networking technologies make possible new home monitoring and p-health solutions. A key element is the information extraction process, which is required for the correct interpretation of the data focused on specific pathologies and applications. This chapter presents a review of the main steps for data preprocessing and for feature extraction in the time, frequency, and information domains. For cardiovascular applications the signal that is usually considered is the electrocardiogram, from which cardiac cycles are detected for further analysis. In particular, the heart rate variability signal is extracted, which provides useful parameters for monitoring of the cardiovascular system. These parameters can be obtained also from photoplethysmograms and ballistocardiograms, which are easily recorded through wearable devices. However, the different natures of the signals and their different time resolutions, as well as nonstationarity due to the ambulatory condition of the users, may lead to a degradation of the extracted information. This chapter also discusses the effects of reduced sampling frequency and suggests strategies for managing nonstationary conditions. Some examples are shown, which are mainly related to sleep analysis.

Signal processing for cardiovascular applications in p-health

Bianchi A. M.;Coelli S.;Lolatto R.;Reali P.;Baselli G.
2022

Abstract

New portable and wearable devices as well as networking technologies make possible new home monitoring and p-health solutions. A key element is the information extraction process, which is required for the correct interpretation of the data focused on specific pathologies and applications. This chapter presents a review of the main steps for data preprocessing and for feature extraction in the time, frequency, and information domains. For cardiovascular applications the signal that is usually considered is the electrocardiogram, from which cardiac cycles are detected for further analysis. In particular, the heart rate variability signal is extracted, which provides useful parameters for monitoring of the cardiovascular system. These parameters can be obtained also from photoplethysmograms and ballistocardiograms, which are easily recorded through wearable devices. However, the different natures of the signals and their different time resolutions, as well as nonstationarity due to the ambulatory condition of the users, may lead to a degradation of the extracted information. This chapter also discusses the effects of reduced sampling frequency and suggests strategies for managing nonstationary conditions. Some examples are shown, which are mainly related to sleep analysis.
Personalized Health Systems for Cardiovascular Disease
9780128189504
ECG
Feature extraction
Frequency information domain
HRV
Preprocessing
Sampling frequency
Signal processing
Time
Time-frequency analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1214597
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