Real-time sleep stage estimation from biological data with trigonometric function regression model

Tomohiro Harada, Fumito Uwano, Takahiro Komine, Yusuke Tajima, Takahiro Kawashima, Morito Morishima, Keiki Takadama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in realtime, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.

Original languageEnglish
Title of host publication2016 AAAI Spring Symposium Series - Collected Papers from the AAAI Spring Symposia
PublisherAI Access Foundation
Pages348-353
Number of pages6
ISBN (Electronic)9781577357544
Publication statusPublished - 2016
Externally publishedYes
Event2016 AAAI Spring Symposium - Palo Alto, United States
Duration: Mar 21 2016Mar 23 2016

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-16-01 - 07

Conference

Conference2016 AAAI Spring Symposium
CountryUnited States
CityPalo Alto
Period3/21/163/23/16

ASJC Scopus subject areas

  • Artificial Intelligence

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