Sleep Insights from the Finger Tip: How Photoplethysmography Can Help Quantify Sleep

Eyal, Shuli; Baharav, A.

doi:10.22489/cinc.2017.274-197

Cited by 8 publications

(15 citation statements)

References 10 publications

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“…Table 8 compares the performance of our model with the PPG based existing alternatives. From Table 8, it is depicted that our proposed model outperforms over other models listed in the Table except [28] and [29]. In terms of accuracy, [28] outperforms the rest of the models in Table 8.…”

Section: Comparison With Existing Ecg Based Modelsmentioning

confidence: 85%

“…Several studies have reported the use of wearable PPG for sleep-wake classification [24][25][26][27][28][29][30][31]. Since the PPG waveform reflects the blood volume changes at the measuring site on the body, features extracted from PPG contain a wide range of physiological information [32,33].…”

Section: Introductionmentioning

confidence: 99%

“…Beattie et al [31] extracted a set of 180 features from actigraphy and PPG for sleep stages classification. Eyal et al [29] applied PPG based time domain, frequency domain, and non-linear features into Bayesian classifier for arousal and wakefulness detection. Fonseca et al [28] applied HRV and body movement features extracted from PPG, and ACM respectively in a multi-class Bayesian linear discriminant classifier for sleep-wake classification.…”

Section: Introductionmentioning

confidence: 99%

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Photoplethysmographic-based automated sleep–wake classification using a support vector machine

Motin¹,

Kamakar²,

Palaniswami³

et al. 2020

Physiol. Meas.

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Objective: Sleep quality has a significant impact on human mental and physical health. The detection of sleep-wake states is thus of paramount importance in the study of sleep. The gold standard method for sleep-wake classification is multisensor based polysomnography (PSG) which is normally recorded in clinical setting. The main drawbacks of PSG are the inconvenience to the subjects, the impact of discomfort on normal sleep cycles, and its requirement for experts' interpretation. In contrast, we aim to design an automated approach for sleep-wake classification using wearable fingertip photoplethysmographic (PPG) signal. Approach: Time domain features are extracted from PPG and PPG based surrogate cardiac signal for sleepwake classification. The minimal redundancy maximal relevance feature selection algorithm is employed to reduce irrelevant and redundant features. Main results: Support vector machine (SVM) based supervised machine learning classifier is then used to classify sleep and wake states. The model is trained using 70% events (6575 sleep-wake events) from the dataset, and the rest 30% events (2818 sleep-wake events) are used for evaluating the performance of the model. Furthermore, the proposed model demonstrates a comparable performance (accuracy 81.10%, sensitivity 81.06%, specificity 82.50%, precision 99.37%, and F score 81.74%) with respect to the existing uni-modal and multi-modal methods for sleep-wake classification. Significance: This result advocates the potential of wearable PPG based sleep-wake classification. A wearable PPG based system would help in continuous, non-invasive monitoring of sleep quality.

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Section: Comparison With Existing Ecg Based Modelsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Photoplethysmographic-based automated sleep–wake classification using a support vector machine

Motin¹,

Kamakar²,

Palaniswami³

et al. 2020

Physiol. Meas.

View full text Add to dashboard Cite

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“…Eyal et al [ 33 ] aimed to validate an automated sleep analysis that was based on the inter-beat-interval (IBI) series obtained from transmissive PPG, and that used features of HRV. The algorithm was tested against the gold standard, PSG.…”

Section: Applications Of Photoplethysmography In Clinical Physiological Measurements In Healthy Subjectsmentioning

confidence: 99%

“…Finally, the area under the curve (AUC)-receiver operating characteristics (ROC) curve is a performance measurement for the classification problems at various threshold settings. Eyal et al [33] aimed to validate an automated sleep analysis that was based on the inter-beat-interval (IBI) series obtained from transmissive PPG, and that used features of HRV. The algorithm was tested against the gold standard, PSG.…”

Section: Ppg In Normal Sleepmentioning

confidence: 99%

Photoplethysmography in Normal and Pathological Sleep

Vulcan

André

Bruyneel

2021

Sensors

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This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.

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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 Insights from the Finger Tip: How Photoplethysmography Can Help Quantify Sleep

Abstract: Abstract

Cited by 8 publications

References 10 publications

Photoplethysmographic-based automated sleep–wake classification using a support vector machine

Photoplethysmographic-based automated sleep–wake classification using a support vector machine

Photoplethysmography in Normal and Pathological Sleep

Automatic sleep-stage scoring based on photoplethysmographic signals

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