Sleep staging classification based on HRV: Time-variant analysis Conference Paper

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Overview

abstract

• An algorithm to evaluate the sleep macrostructure based on heart rate fluctuations from ECG signal is presented. This algorithm is an attempt to evaluate the sleep quality out of sleep centers. The algorithm is made up by a) a time-variant autoregressive model used as feature extractor and b) a hidden Markov model used as classifier. Characteristics coming from the joint probability of HRV features were used to fed the HMM. 17 full polysomnography recordings from healthy subjects were used in the current analysis. When compared to Wake-NREM-REM given by experts, the automatic classifier achieved a total accuracy of 78.21±6.44%25 and a kappa index of 0.41±.1085 using two features and a total accuracy of 79.43±8.83%25 and kappa index of 0.42±.1493 using three features. ©2009 IEEE.

authors

• Aletti F.
• Bianchi A.M.
• Cerutti S.
• Matteucci M.
• Méndez García Martin Oswaldo

publication date

• 2009-01-01

published in

• Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009  Proceedings

Research

keywords

• Hidden Markov models; Markov processes; Sleep research; Auto regressive models; Automatic classifiers; Current analysis; Feature extractor; Healthy subjects; Heart rate fluctuations; Joint probability; Macrostructures; Quality control

Identity

Digital Object Identifier (DOI)

• 10.1109/IEMBS.2009.5332624

PubMed ID

• 19963449

Additional Document Info

start page

• 9

end page

• 12