Noncontact Pressure-Based Sleep/Wake Discrimination

Walsh, Lorcan; McLoone, Seán; Ronda, Joseph M.; Duffy, Jeanne F.; Czeisler, Charles A.

doi:10.1109/tbme.2016.2621066

Cited by 21 publications

(12 citation statements)

References 38 publications

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“…Although validations adhering to best-practice standards are few, those that are draw the same basic conclusions about commercial device performance: available devices are limited in their specificity, particularly after sleep onset. 2,[8][9][10][11][13][14][15][16][17][18][19] Marketing of wearable sleep tracking devices has now progressed to claim that these devices can identify sleep stages, despite the absence of empirical support for their ability to accurately detect sleep/wake. [20][21][22] A unique category of sleep monitoring devices, non-contact bedside radiofrequency biomotion sensors (NRBS), use remote biosensing technologies, largely circumventing interference caused by even light instrumentation during sleep.…”

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confidence: 99%

Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device

Schade

Bauer

Murray

et al. 2019

Journal of Clinical Sleep Medicine

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Study Objectives: To assess the sleep detection and staging validity of a non-contact, commercially available bedside bio-motion sensing device (S+, ResMed) and evaluate the impact of algorithm updates. Methods: Polysomnography data from 27 healthy adult participants was compared epoch-by-epoch to synchronized data that were recorded and staged by actigraphy and S+. An update to the S+ algorithm (common in the rapidly evolving commercial sleep tracker industry) permitted comparison of the original (S+V1) and updated (S+V2) versions. Results: Sleep detection accuracy by S+V1 (93.3%), S+V2 (93.8%), and actigraphy (96.0%) was high; wake detection accuracy by each (69.6%, 73.1%, and 47.9%, respectively) was low. Higher overall S+ specificity, compared to actigraphy, was driven by higher accuracy in detecting wake before sleep onset (WBSO), which differed between S+V2 (90.4%) and actigraphy (46.5%). Stage detection accuracy by the S+ did not exceed 67.6% (for stage N2 sleep, by S+V2) for any stage. Performance is compared to previously established variance in polysomnography scored by humans: a performance standard which commercial devices should ideally strive to reach. Conclusions: Similar limitations in detecting wake after sleep onset (WASO) were found for the S+ as have been previously reported for actigraphy and other commercial sleep tracking devices. S+ WBSO detection was higher than actigraphy, and S+V2 algorithm further improved WASO accuracy. Researchers and clinicians should remain aware of the potential for algorithm updates to impact validity.

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confidence: 99%

Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device

Schade

Bauer

Murray

et al. 2019

Journal of Clinical Sleep Medicine

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“…Walsh et al presented the evaluation of an under-mattress sleep monitoring system for non-contact sleep/wake discrimination, which compared different classifiers (SVM, KNN, ANN and LDA) based on the extracted temporal, spatial, and statistical features [16]. By using electroencephalogram (EEG) signals, the structural graph similarity and the k-means (SGSKM) are combined to identify six sleep stages, and four existing methods and the support vector machine (SVM) classifier were compared with the proposed method [17].…”

Section: B Sleep Stage Recognitionmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.

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“…Most people now live with a lack of sleep and tend to ignore the importance of sleep quality and sleep patterns, which can eventually lead to sleep disorders. Throughout the years, a number of devices has been launched to provide users with knowledge of their sleep hygiene [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Noncontact Sleep Study by Multi-Modal Sensor Fusion

Chung

Song

Shin³

et al. 2017

Sensors

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Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

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Noncontact Pressure-Based Sleep/Wake Discrimination

Cited by 21 publications

References 38 publications

Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device

Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Noncontact Sleep Study by Multi-Modal Sensor Fusion

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