The aim of our study is to identify parameters able to predict the onset of falling asleep. Firstly we have considered the balance of low frequency versus high frequency in PSD. A set of experiments demonstrates that, when a person tries to resist falling asleep, the LF/HF ratio of PSD computed from the HRV signal increases significantly a few minutes before becoming significantly lower during sleep state. Like a reaction to falling asleep, it causes high activity of the sympathetic system while the parasympathetic system decreases its activity: we can call this phenomenon a micro arousal. There are several methods for performing predictions. We use fuzzy decision logic to model the oncoming onset of sleep. The whole system consists of a signal acquisition and preprocessing subsystem, a feature extraction subsystem, and a fuzzy logic decision module to predict.

Heart rate variability analysis for prediction of sleep onset in car drivers / M. Malcangi, S. Smirne. - In: JOURNAL OF SLEEP RESEARCH. - ISSN 0962-1105. - Volume 21:Supplement 1(2012 Sep), pp. 307-308.

Heart rate variability analysis for prediction of sleep onset in car drivers

M. Malcangi
Primo
;
S. Smirne
Ultimo
2012

Abstract

The aim of our study is to identify parameters able to predict the onset of falling asleep. Firstly we have considered the balance of low frequency versus high frequency in PSD. A set of experiments demonstrates that, when a person tries to resist falling asleep, the LF/HF ratio of PSD computed from the HRV signal increases significantly a few minutes before becoming significantly lower during sleep state. Like a reaction to falling asleep, it causes high activity of the sympathetic system while the parasympathetic system decreases its activity: we can call this phenomenon a micro arousal. There are several methods for performing predictions. We use fuzzy decision logic to model the oncoming onset of sleep. The whole system consists of a signal acquisition and preprocessing subsystem, a feature extraction subsystem, and a fuzzy logic decision module to predict.
Onset of falling asleep ; heart rate variability ; biomedical signal processing ; fuzzy logic
Settore INF/01 - Informatica
set-2012
Article (author)
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

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

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/204650
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 4
social impact