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.
2022-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
