Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography

Tal, Asher; Shinar, Zvika; Shaki, David; Codish, Shlomi; Goldbart, Aviv

doi:10.5664/jcsm.6514

Cited by 114 publications

(70 citation statements)

References 24 publications

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“…Part of the systems accuracy may be attributed to its sleep wake detection [3], which many of the screening devices lack. This accuracy is comparable to FDA approved systems for OSA diagnosis [6] Being contact free makes this system suited for home use and long-term monitoring to assess sleep apnea in the natural patient's environment.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Analysis of the superimposed waveform allows separation into its 3 components and extraction of motion, respiratory rate, and inter-beat-intervals. The accuracy and validity of measuring these basic vitals is described elsewhere [3,4]. Using movement, Heart-Rate-Variability analysis, and respiration rate variability the system detects sleep and wake as described in a paper submitted to the Journal of Clinical Sleep Medicine [3].…”

Section: Sleep/wake Detection Algorithmmentioning

confidence: 99%

“…These screening devices do not detect sleep and wake, or use only movement to do so. In this study we used a system with an established accuracy for sleep wake detection [3], and tested a novel algorithm that detects sleep apnea. The combination of a contact free under-the-mattress measurement with good sleep wake and apnea detection makes the system appealing for screening purposes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sleep Apnea Screening with a Contact:Free Under:the:Mattress Sensor

Davidovich

Karasik

Tal

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Sleep/wake Detection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sleep Apnea Screening with a Contact:Free Under:the:Mattress Sensor

Davidovich

Karasik

Tal

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…Embedding different types of ambient sensors into objects that we interact daily is more attractive than using multiple redundant sensors collecting homogeneous information. Embedded devices, such as bed sensors, have been developed to track different sleep-related metrics, such as sleep time, breathing, snoring, heart rate, body and room temperature or humidity levels [49][50][51] . Whilst these sensors are interesting and potentially valuable for clinical and epidemiological research, as well as wellness and sleep education, very little is known about how their performance against gold-standard measures and more research is required to evaluate their usability.…”

Section: Sleep Monitoring Outside the Laboratorymentioning

confidence: 99%

The future of sleep health: a data-driven revolution in sleep science and medicine

et al. 2020

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In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-ofthe-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

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“…Wearable devices increasingly measure ECG and respiration [10][11][12] . Cardiorespiratory signals may be obtainable in a number of ways, including contact recordings such as Withings, ballistocardiogram 13 , or non-contact radar-type applications such as EarlySense 14 , and SleepScore 15 , etc. On the other hand, EEG can be highly abnormal in medically ill populations, making standard analysis difficult 16 .…”

Section: Introductionmentioning

confidence: 99%

Sleep staging from electrocardiography and respiration with deep learning

Sun

Ganglberger

Panneerselvam

et al. 2019

Sleep

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Study Objective:Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. Therefore, we hypothesize that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.Methods: Using a dataset including 8,682 polysomnographs, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals.Results: ECG in combination with the abdominal respiratory effort achieve the best performance for staging all five sleep stages with a Cohen's kappa of 0.600 (95% confidence interval 0.599 -0.602); and 0.762 (0.760 -0.763) for discriminating awake vs. rapid eye movement vs. non-rapid eye movement sleep. The performance is better for young participants and for those with a low apnea-hypopnea index, while it is robust for commonly used outpatient medications. Conclusions:Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large population. It opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible, such as in critically ill patients. Deep Network ArchitectureWe trained five deep neural networks based on the following input signals and their combinations: 1) ECG; 2) CHEST (chest respiratory effort); 3) ABD (abdominal respiratory effort); 4) ECG+CHEST; and 5) ECG+ABD. Each deep neural network contained a feed-forward convolutional neural network (CNN) which learned features pertaining to each epoch, and a recurrent neural network (RNN), in this case long-short term memory (LSTM), to learn temporal patterns among consecutive epochs.The CNN of the network is similar to that in Hannun et al. 20 . As shown in Figure 1A and Figure 1B, the network for a single type of input signal, i.e. ECG, CHEST or ABD, consists of a convolutional layer, several residual blocks and a final output block. For a network with both ECG and CHEST/ABD as input signals ( Figure 1C), we first fixed the weights of the layers up to the 9 th residual block (gray) for the ECG network and similarly fixed up to the 5 th residual block (gray) for the CHEST/ABD network, concatenated the outputs, and then fed this concatenation into a subnetwork containing five residual blocks and a final output block. The numbers of fixed layers were chosen so that the outputs of layers from different modalities have the same shape (after padding zeros), and were then concatenated.The LSTM of the network has the same structure for different input signals. It is a bi-directional LSTM, where the context cells from the forward and backward directions are concatenated. For the network

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Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography

Cited by 114 publications

References 24 publications

Sleep Apnea Screening with a Contact:Free Under:the:Mattress Sensor

Sleep Apnea Screening with a Contact:Free Under:the:Mattress Sensor

The future of sleep health: a data-driven revolution in sleep science and medicine

Sleep staging from electrocardiography and respiration with deep learning

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