Pulse transit time and heart rate variability in sleep staging

Shahrbabaki, Sobhan Salari; Ahmed, Beena; Penzel, Thomas; Cvetković, Dean

doi:10.1109/embc.2016.7591475

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Of all the reviewed articles, only 21 performed classification of all the sleep stages defined by either of these guidelines [ 24 , 39 , 42 , 45 , 50 , 58 , 67 , 72 , 76 , 80 , 88 , 89 , 90 , 93 , 101 , 102 , 105 , 108 , 109 , 117 , 121 ]. Amongst them, all but three [ 24 , 58 , 150 ] used EEG signals for classification, where the difference between sleep stages is known to be most obvious. In [ 150 ], where only PPG signals are used with accelerometry data to detect movements, the overall accuracy is quite low.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

119

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Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

119

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“…However, EEG recording requires the patients to wear scalp electrode caps, which are uncomfortable during sleeping. Another option is the combination of ECG, respiration, accelerometer (ACM) or actigraphy, which is an alternative to the EEG-based methods [14][15][16][17][18][19]. However, this increases the complexity of the recording and assessment.…”

Section: Introductionmentioning

confidence: 99%

Multi-stage sleep classification using photoplethysmographic sensor

et al. 2023

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The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.

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“…Cardio-respiratory features extracted from ECG and respiratory signal using wearable sensors for sleep-wake classification have been explored in [10][11][12][13][14]. Though these ECG based multi-modal approaches reduced the burden of using the scalp electrodes as used in EEG, the multi-sensor approach is still not comfortable to patients for long term screening especially for infants and elderly persons.…”

Section: Introductionmentioning

confidence: 99%

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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Pulse transit time and heart rate variability in sleep staging

Cited by 8 publications

References 13 publications

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Multi-stage sleep classification using photoplethysmographic sensor

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

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