Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification

Aktaruzzaman, Md.; Rivolta, Massimo W.; Karmacharya, Ruby; Scarabottolo, Nello; Pugnetti, Luigi; Garegnani, Massimo; Bovi, G.; Scalera, Giovanni Marco; Ferrarin, Maurizio; Sassi, Roberto

doi:10.1016/j.compbiomed.2017.08.006

Cited by 40 publications

(30 citation statements)

References 33 publications

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“…There are several important advantages in using a chest/neck placement of the actigraphy vs. wrist actigraphy. First, the possibility to acquire additional signals from a chest/neck location, such as heart rate variability, needs to be mentioned [42]. Also, a chest/neck actigraphy can add additional data on sleep position unlike wrist actigraphy [43].…”

Section: Discussionmentioning

confidence: 99%

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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This study aims to evaluate the accuracy of a wireless tracheal audio sensor including the sleep body position measurement, as a possible tool to screen for obstructive sleep apnea (OSA) and also to distinguish among "positional" and "non-positional" sleep apnea patients. 30 adult subjects were asked to sleep naturally for a single night in the sleep laboratory, having simultaneous polysomnography (PSG) as a reference. The results were compared using, e.g., Pearson's and Lin's correlation coefficients, Bland-Altman plots, mean absolute error, intercept of the fitted linear model etc. We found the thresholding approach performed on normalized audio energy data achieved mean absolute error of 5.7, and average difference was −2.0. Pearson's and Lin's R coefficients were 0.92 and 0.70, respectively. The qualitative approach reached 86% accuracy (sensitivity of 96%, and specificity of 76%) when setting a binary border at the level of AHI=15 (combined normal + mild versus combined moderate + severe cases). We also checked the distribution of apneas depending on the body position. Our sensor showed a mean ± SE difference between supine apnea index and non-supine apnea index of 8.3 ± 3.2, while PSG reference of 11.2 ± 3.8. The proposed sensor might be a good complement for home sleep studies, being less disturbing and allowing for longitudinal observations and reliably showing positional OSA. PSG is the gold standard in diagnosing OSA; however, it requires to spend a night in a lab with medical staff, it provides short observation, the cost of the study is very high, and it is less suitable for children. This is why a reliable screening method is needed in sleep medicine. INDEX TERMS Obstructive sleep apnea, apnea-hypopnea index, home sleep study, tracheal sounds, audio processing, supine sleep, non-supine sleep

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Section: Discussionmentioning

confidence: 99%

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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“…Long et al [6] reported 95.7% accuracy and a kappa of 0.59 for a sleep-wakefulness classification by a combination of actigraphy and respiration in a study of 15 healthy subjects. Additionally, Aktaruzzaman et al [9] have recently reported classification performance by a combination of actigraphy and HRV in 18 subjects with no previous history of sleep disorders. They found that sleep and waking were distinguished at 78% accuracy by four features derived only from wrist actigraphy, and that the addition of HRV features resulted in no significant improvement of classification performance.…”

Section: Discussionmentioning

confidence: 99%

“…To utilize these data for self-healthcare, the detection of sleep period and assessment of sleep quality are important issues. Although plenty of earlier studies have reported various algorithms and methods for this purpose, most of them used either heartbeat or actigraphic data [1][2][3][4][5][6][7][8], and only a few of them reported the combined use of both parameters in a small population [9]. Because many recent wearable sensors of electrocardiogram (ECG)/pulse wave also equip triaxial accelerometers, algorithms utilizing both data modalities seem useful.…”

Section: Introductionmentioning

confidence: 99%

Sleep Stage Classification by a Combination of Actigraphic and Heart Rate Signals

Yuda

Yoshida

Sasanabe

et al. 2017

JLPEA

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Abstract:Although heart rate variability and actigraphic data have been used for sleep-wake or sleep stage classifications, there are few studies on the combined use of them. Recent wearable sensors, however, equip both pulse wave and actigraphic sensors. This paper presents results on the performance of sleep stage classification by a combination of heart rate and actigraphic signals. We studied 40,643 epochs (length 3 min) of polysomnographic data in 289 subjects. A combined model, consisting of autonomic functional indices from heart rate variability and body movement indices derived from actigraphic data, discriminated non-rapid-eye-movement (REM) sleep from waking/REM sleep with 76.9% sensitivity, 74.5% specificity, 75.8% accuracy, and a Cohen's kappa of 0.514. The combination was also useful for discriminating between REM sleep and waking at 77.2% sensitivity, 72.3% specificity, 74.5% accuracy, and a kappa of 0.491.

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“…A combination of actigraphy with other features such as heart rate variability may improve the accuracy of sleep/ wakefulness detection. [28][29][30] Our proposed method extracts features from body movement which are recorded by tracheal movements (Spike30s and Spike1h features) to simulate actigraphy features. The importance of these results is that a combination of body movement with respiratory related sounds that can be recorded over the trachea by a portable and convenient sleep screening device can have significant clinical applications beyond sleep detection, such as assessment of respiration and severity of sleep apnea.…”

Section: Dovepressmentioning

confidence: 99%

<p>Sleep/Wakefulness Detection Using Tracheal Sounds and Movements</p>

Ghahjaverestan¹,

Akbarian²,

Hafezi³

et al. 2020

NSS

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Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification

Cited by 40 publications

References 33 publications

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

Sleep Stage Classification by a Combination of Actigraphic and Heart Rate Signals

<p>Sleep/Wakefulness Detection Using Tracheal Sounds and Movements</p>

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