Design and First Evaluation of a Sleep Characterization Monitoring System Using a Remote Contactless Sensor

Guettari, Toufik; Istrate, Dan; Boudy, Jérôme; Benkelfet, Badr-Eddine; Fumel, Betty; Daviet, Jean-Christophe

doi:10.1109/jbhi.2016.2639823

Cited by 16 publications

(22 citation statements)

References 46 publications

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“…This device is based mainly on the measurement of the thermal radiation emanating from the patient. Our study was carried out within the framework of the improvement of the device developed in the study proposed by the author of [25], and our final objective is to integrate the device in the measurement chain developed within the Smart-EEG [26].…”

Section: Discussionmentioning

confidence: 99%

“…The authors of [25] developed a device based on the use of a thermopile sensor for the detection of the presence of a patient in a bed. This study [25] conducted in a hospital setting allowed the author to extend the application of the thermopile sensor to detect the stages of sleep by exploiting the variation in the thermal signature during the night. Based on this information, the author claims to distinguish deep sleep from agitated sleep.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we describe a method that follows the same approach as that used in [25]: an analysis of sleep in the sense of physical activity, analyzing the evolution of human thermal radiation during overnight sleep. This thermal radiation is due to the internal heat of the human body whose core temperature is fixed around 37.2 • C. Its variation outside the body is produced by the influence of the ambient heat.…”

Section: Introductionmentioning

confidence: 99%

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Thermal-Signature-Based Sleep Analysis Sensor

et al. 2017

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This paper addresses the development of a new technique in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90 min each. Each of these cycles is composed of a succession of several stages of sleep that vary in depth. Analysis of sleep is usually done via polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG), and chin muscle tone by electromyography (EMG). Recordings are made mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30s epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: "awakening," "calm sleep," and "restless sleep". The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYEL-SYstèmes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thermal-Signature-Based Sleep Analysis Sensor

et al. 2017

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“…For the same sleep, the scoring results obtained from R&K rules and the AASM rules will be slightly different. One study [19] adopted both rules to score PSG sleep recordings of healthy subjects and patients (38 The differences between the results of the 2 sleep standards can be attributed to the different rules used [20].…”

Section: Sleep Stages Scoring Rulesmentioning

confidence: 99%

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Pan¹,

Brulin²,

Campo³

2020

JMIR Biomed Eng

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Background Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

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“…A different approach was presented by Guettari et al [86], determining the presence in bed by feeding the difference between the ambient and the radiated temperature as features for a clustering based analysis, performed by k-medoids, and classify considering a threshold. The developed sleep quality estimation algorithm operates if the subject was detected in the bed, extracting features from the difference signal using a symbolic approximation with four symbols (based in the SAX method) and detects if the subject is awake, sleeping agitated or in paradoxical sleep using a Kohonen network (a kind of self-organizing network).…”

Section: F Proposed New Metricsmentioning

confidence: 99%

A Review of Approaches for Sleep Quality Analysis

Mendonça¹,

Mostafa²,

Morgado‐Dias³

et al. 2019

IEEE Access

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Sleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term ''sleep quality'' still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been proposed to address the estimation of sleep quality, and devices were developed to conduct the examination in the subject's home. The objective of this paper is to analyze the methods and the devices presented in the literature, assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding, leading to developments in the treatment of sleep quality deficits. A systematic review was conducted, selecting research articles published from 2000 to 2018, and two research questions were formulated, specifically, ''what methods for sleep quality assessment have been developed'' and ''what kind of measures are employed by the devices that have been developed to estimate sleep quality.'' The research trend for the assessment of sleep quality is based on the sleep macrostructure, and it was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined. A detailed resume of the key findings and the identified challenges are presented, ascertaining the main gaps in the current state of the art. INDEX TERMS Sleep quality analysis, monitoring devices, home detection.

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Design and First Evaluation of a Sleep Characterization Monitoring System Using a Remote Contactless Sensor

Cited by 16 publications

References 46 publications

Thermal-Signature-Based Sleep Analysis Sensor

Thermal-Signature-Based Sleep Analysis Sensor

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

A Review of Approaches for Sleep Quality Analysis

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