Key result
An automatic sleep staging classifier based on heart rate variability achieved a total accuracy of 78.21% and a kappa index of 0.41 when compared to expert staging.
Why the study?
Does an algorithm based on heart rate variability accurately classify sleep stages compared to expert evaluation in healthy subjects?
Does an algorithm based on heart rate variability accurately classify sleep stages compared to expert evaluation in healthy subjects?
An HRV-based algorithm using a hidden Markov model can classify sleep stages with nearly 80% accuracy compared to expert evaluation, offering a potential tool for out-of-center sleep quality assessment.
Answers are grounded in this paper and cite their sources.
May support out-of-center sleep staging in healthy adults; hypothesis-generating before clinical validation or adoption.
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% and a kappa index of 0.41+/-.1085 using two features and a total accuracy of 79.43+/-8.83% and kappa index of 0.42+/-.1493 using three features.
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Méndez et al. (2009) studied Healthy subjects (sleep staging) (n=17). Automatic sleep staging classifier based on HRV vs. Expert staging was evaluated on Total accuracy of sleep staging classification. An automatic sleep staging classifier based on heart rate variability achieved a total accuracy of 78.21% and a kappa index of 0.41 when compared to expert staging.
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