Ballistocardiography for nonintrusive sleep structure estimation

Park, Kwang Suk; Hwang, Sang Hyun; Jung, Da Woon; Yoon, Hye-Kyung; Lee, Won Kyu

doi:10.1109/embc.2014.6944793

Cited by 7 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, sleep monitoring can be carried out by trained sleep technicians assisting participants with in-home portable devices [2] . To date, several in-home sleep monitoring methods, such as single-channel EEG [12] , ballistocardiogram [13] , photoplethysmography [14] and actigraphy [15] , have been developed. Among them, single-channel EEG, such as UMindSleep [16] , provides only coarse-grained information for sleep monitoring and thus cannot generate sufficiently accurate sleep assessment results.…”

Section: Introductionmentioning

confidence: 99%

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses

Ying

Wang

Zhao

et al. 2023

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

Objective: A novel in-home sleep monitoring system with an 8-channel biopotential acquisition front-end chip is presented and validated via multilevel data analyses and comparision with advanced polysomnography. Methods and procedures: The chip includes a cascaded low-noise programmable gain amplifier (PGA) and 24-bit Σ-Δ analog-to-digital converter (ADC). The PGA is based on three op-amp structure while the ADC adopts cascade of integrator feedforward and feedback (CIFF-B) architecture. An innovative chopper-modulated input-scaling-down technique enhances the dynamic range. The proposed system and commercial polysomnography were used for in-home sleep monitoring of 20 healthy participants. The consistency and significance of the two groups' data were analyzed. Results: Fabricated in 180 nm BCD technology, the input-referred noise, input impedance, common-mode rejection ratio, and dynamic range of the acquisition front-end chip were 0.89 µVpp, 1.25 GΩ, 113.9 dB, and 119.8 dB. The kappa coefficients between the sleep stage labels of the three scorers were 0.80, 0.76, and 0.79. The consistency of the slowing index, multiscale entropy, and percentile features between the two devices reached 0.958, 0.885, and 0.834. The macro sleep architecture characteristics of the two devices were not significantly different (all p>0.05). Conclusion: The proposed chip was applied to develop an in-home sleep monitoring system with significantly reduced size, power, and cost. Multilevel analyses demonstrated that this system collects stable and accurate in-home sleep data. Clinical impact: The proposed system can be applied for long-term in-home sleep monitoring outside of laboratory environments and sleep disorders screening that with low cost.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses

Ying

Wang

Zhao

et al. 2023

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

show abstract

Various Physiological Methods to Identify Sleep Onset

Gan

Huang

et al. 2022

4th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

View full text Add to dashboard Cite

Sleep stages classification using cardio-respiratory variables

Gasmi

Augusto

Beaudet³

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

View full text Add to dashboard Cite

Analysis of sleep is important in order to detect health issues and try to prevent them. In particular, sleep dysfunctions may be the first signs of cognitive frailties for elderly persons. The polysomnography (PSG) is considered the golden standard to perform a comprehensive sleep analysis, as it is based on several sensors placements. However, for longitudinal study of sleep that is required to prevent frailty for elderly persons, such medical equipment is not suitable since it is very invasive. Recent technological advances in sensors allow to gather data with a good precision with less intrusive equipment. The main objective of this study consists in developing a new algorithmic approach to analyse sleep using data from low intrusive sensors. In this study we focus on sleep phase detection, i.e. wake, Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM). We consider the following sources of data: heart beat rate, as well as user data such as gender, age, etc. The problem is considered as a supervised classification machine learning problem. We propose a benchmark of several machine learning algorithms and compare their performances against the medical gold standard, the PSG. To do so, we use a data-set collected from a published clinical trial. Support Vector Machine (SVM) algorithm globally outperforms all other methods with a 76.5% agreement with the PSG. As a direct perspective of this study, we plan to add other sources of data using custom sensors to improve the performance of the prediction.

show abstract

Ballistocardiography for nonintrusive sleep structure estimation

Cited by 7 publications

References 10 publications

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses

Various Physiological Methods to Identify Sleep Onset

Sleep stages classification using cardio-respiratory variables

Contact Info

Product

Resources

About