Analyzing respiratory effort amplitude for automated sleep stage classification

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Abstract

Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen's Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance. Keywords Respiratory effort amplitude; Signal calibration; Feature extraction; Sleep stage classification
Original languageEnglish
Pages (from-to)197-205
Number of pages9
JournalBiomedical Signal Processing and Control
Volume14
DOIs
Publication statusPublished - 2014

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Sleep Stages
Sleep
REM Sleep
Eye movements
Tidal Volume
Eye Movements
Calibration
Light
Frequency bands
Feature extraction
Classifiers

Cite this

@article{224fa37ebf8144d081ec3e06caeb4f74,
title = "Analyzing respiratory effort amplitude for automated sleep stage classification",
abstract = "Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen's Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8{\%}) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2{\%}). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance. Keywords Respiratory effort amplitude; Signal calibration; Feature extraction; Sleep stage classification",
author = "X. Long and J. Foussier and P. Fonseca and R. Haakma and R.M. Aarts",
year = "2014",
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Analyzing respiratory effort amplitude for automated sleep stage classification. / Long, X.; Foussier, J.; Fonseca, P.; Haakma, R.; Aarts, R.M.

In: Biomedical Signal Processing and Control, Vol. 14, 2014, p. 197-205.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Analyzing respiratory effort amplitude for automated sleep stage classification

AU - Long, X.

AU - Foussier, J.

AU - Fonseca, P.

AU - Haakma, R.

AU - Aarts, R.M.

PY - 2014

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N2 - Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen's Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance. Keywords Respiratory effort amplitude; Signal calibration; Feature extraction; Sleep stage classification

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