Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement

Hwang, Sang Hyun; Lee, Eunice; Jeong, Do Un; Park, Kwang S.

doi:10.3414/me15-01-0140

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…26 reported an accuracy of 81% using a Doppler radar system to capture cardiorespiratory activity; and Hwang et al . 27 reported a Cohen’s κ of 0.48 and an accuracy of 70.9% using body movement and respiratory dynamics.…”

Section: Introductionmentioning

confidence: 99%

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Radha

Fonseca

Moreau³

et al. 2019

Sci Rep

134

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Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

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Section: Introductionmentioning

confidence: 99%

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Radha

Fonseca

Moreau³

et al. 2019

Sci Rep

134

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show abstract

“…These devices measure some biological signals which changes with sleep stage change such as Electrocardiogram (ECG) and respiratory movement. For example, Hwang and et al developed a mattress-type sensor and estimated sleep stage by measuring body movement and respiration signals with an accuracy close to 79.0% [6]. Kagawa et al estimated sleep stage using body movement and respiratory interval measured by a multiple radar sensor and their average estimation accuracy was 71.9% [7].…”

Section: Introductionmentioning

confidence: 99%

Sleep stage estimation method using a camera for home use

et al. 2019

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Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and five parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these five parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed sufficient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.

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“…Compared to PSG results, the method classified the four sleep stages with an average accuracy of 70.9% and kappa of 0.48, which are comparable to those of ambulatory PSG devices. No significant differences were observed in the detection performances between the 12 normal and 13 OSA subject groups [26].…”

Section: Sleep Stage Estimation With Respiratory Signalsmentioning

confidence: 77%

“…These films are also appropriate for unobtrusively detecting mechanical loads caused by movements in bed. Thin PVDF film sensors located just under the bed cover sheet were used for sleep detection [26]. Based on body movement recordings, these sensors estimated sleep with 92.9% sensitivity, 62.0% specificity, and 89.2% accuracy for subjects whose sleep efficiency is higher than 80%.…”

Section: Environmental Devicesmentioning

confidence: 99%

“…With PVDF film sensors located just beneath the bed cover sheet, apneic events were detected with an accuracy of 85%, and the estimated AHI showed a correlation of 0.94 with the AHI derived from PSG [36]. A PVDF film sensor size of 30 × 30 cm used for body movement detection was also used for estimating sleep stages unobtrusively based on recorded respiratory movements [26]. The respiratory movements were extracted from the PVDF data, and four stages of sleep (Wake, Light, SWS, and REM) were estimated based on the relative magnitudes and variabilities in the respiratory frequency.…”

Section: Sleep Stage Estimation With Respiratory Signalsmentioning

confidence: 99%

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Smart technologies toward sleep monitoring at home

Park

Choi

2019

Biomed. Eng. Lett.

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With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea-hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.

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Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement

Abstract: The developed system and methods can be applied to a home sleep monitoring system.

Cited by 15 publications

References 28 publications

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Sleep stage estimation method using a camera for home use

Smart technologies toward sleep monitoring at home

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