Sleep-wake stages classification based on heart rate variability

Werteni, Hayet; Yacoub, Slim

doi:10.1109/bmei.2012.6513040

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…In addition, sleeping in a sleep laboratory and sleeping at home in a familiar environment are two different situations. These However, there are several scientific studies, confirming the relationship between the movement, breathing and heart rate with the sleep stages [7,8,9]. And these parameters can be obtained in a more comfortable way, than the PSG [10].…”

Section: Introductionmentioning

confidence: 88%

“…The article of [7] presents an approach for the identification of Wake and Sleep states using the ECG signal and a neural network-based algorithm. 16 PSG records from the MIT-BIH database were used for the evaluation.…”

Section: State Of the Artmentioning

confidence: 99%

“…The main aim of presented project work is to develop a software system supporting a recognition of Sleep/Wake states when analyzing few human body signals, which could be obtained in a non-obtrusive way. According to [7,10,11,12] heart rate, breathing and movement data are qualified signals.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Sleep/Wake States analyzing Heart Rate, Breathing and Movement Signals

Gaiduk

Seepold

Penzel

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This document presents an algorithm for a nonobtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen's kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.

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Section: Introductionmentioning

confidence: 88%

Section: State Of the Artmentioning

confidence: 99%

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Recognition of Sleep/Wake States analyzing Heart Rate, Breathing and Movement Signals

Gaiduk

Seepold

Penzel

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

show abstract

“…Moreover, the fact, that different sleep stages have an appreciable effect on heart rate, breathing and movement (Hayet and Slim 2012, Kurihara and Watanabe 2012, Long et al 2014, provides good reason to combine these parameters to develop the sleep stage classification algorithm.…”

Section: Motivationmentioning

confidence: 99%

Automatic sleep stages classification using respiratory, heart rate and movement signals

et al. 2018

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The human body is more fragile than people think. In order to survive, it requires sleep just as much as food, water, or oxygen. This is a basic principle of human physiology that has been borne out by thousands of research studies.In general, sleep is a state where our bodies and minds rest and rejuvenate (Spriggs 2009). It is obligatory for our normal physiological, mental and emotional functioning during awake hours. The belief that it is possible to have just a couple of hours of sleep a night over a long period of time without suffering any negative consequences is a common misconception (National Heart Lung and Blood Institute (NHLBI) 2011). It is categorically beyond doubt that when sleep contains even slight abnormalities, the aftermath can lead to physical illness, psychological problems or an untimely death (Lee-Chiong et al 2012).A popular misconception is that adults have to sleep at least 7-8 h every night to be rejuvenated properly, while children require far more hours of sleep (National Heart Lung and Blood Institute (NHLBI) 2011). However, this is only standard recommended advice; sleep requirements are individual for every person (National Heart Lung and Blood Institute (NHLBI) 2011). In addition, getting many hours of sleep does not always guarantee a healthy and rested state, because the crucial point here is not the quantity, but the quality (National Heart Lung and Blood Institute (NHLBI) 2011).

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“…Yucelbas et al used a morphological method to extract features from ECG signals for classification of three sleep stages (wake, NREM, and REM) and achieved accuracies of 87.11% and 77.02% for two datasets of healthy individuals using a random forest classifier [22]. Hayet et al decomposed heart rate variability (HRV) features from ECG signals to establish a sleep-wake classification system with the ELM algorithm [23]. However, for physiological electrical signals, the placement of electrodes requires professional guidance.…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep-stage scoring based on photoplethysmographic signals

Wu¹,

Yang²,

Pan³

et al. 2020

Physiol. Meas.

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Objective: Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed. Approach: To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO2 features from PPG signals. An artificial neural network classifier was used to integrate the results of ten binary support vector machine classifiers and realise sleep-stage classification. Leave-one-subject-out validation was applied to evaluate our proposed model. Main results: Thirty-one subjects were enrolled in the study, in which 21 subjects were with high sleep quality (sleep efficiencies ⩾85%). Our model achieved accuracies of 57% (κ = 0.39), 62% (κ = 0.41), and 78% (κ = 0.54) for the classification of five sleep stages (wake, N1, N2, N3, and rapid eye movement (REM) sleeps), four sleep stages (wake, light, deep, and REM sleeps) and three sleep stages (wake, non-rapid eye movement (NREM), and REM sleeps), respectively. For the remaining ten subjects with poor sleep quality, the results came to 55% (κ = 0.39), 62% (κ = 0.43), and 75% (κ = 0.52). Significance: The satisfactory performance of our proposed model reveals the potential of PPG signals for sleep-stage scoring, which may contribute to automatic sleep monitoring in the home environment.

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Sleep-wake stages classification based on heart rate variability

Cited by 18 publications

References 7 publications

Recognition of Sleep/Wake States analyzing Heart Rate, Breathing and Movement Signals

Recognition of Sleep/Wake States analyzing Heart Rate, Breathing and Movement Signals

Automatic sleep stages classification using respiratory, heart rate and movement signals

Automatic sleep-stage scoring based on photoplethysmographic signals

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