Literature DB >> 24693921

Robust sleep quality quantification method for a personal handheld device.

Hangsik Shin1, Byunghun Choi, Doyoon Kim, Jaegeol Cho.   

Abstract

OBJECTIVE: The purpose of this study was to develop and validate a novel method for sleep quality quantification using personal handheld devices.
MATERIALS AND METHODS: The proposed method used 3- or 6-axes signals, including acceleration and angular velocity, obtained from built-in sensors in a smartphone and applied a real-time wavelet denoising technique to minimize the nonstationary noise. Sleep or wake status was decided on each axis, and the totals were finally summed to calculate sleep efficiency (SE), regarded as sleep quality in general. The sleep experiment was carried out for performance evaluation of the proposed method, and 14 subjects participated. An experimental protocol was designed for comparative analysis. The activity during sleep was recorded not only by the proposed method but also by well-known commercial applications simultaneously; moreover, activity was recorded on different mattresses and locations to verify the reliability in practical use. Every calculated SE was compared with the SE of a clinically certified medical device, the Philips (Amsterdam, The Netherlands) Actiwatch.
RESULTS: In these experiments, the proposed method proved its reliability in quantifying sleep quality. Compared with the Actiwatch, accuracy and average bias error of SE calculated by the proposed method were 96.50% and -1.91%, respectively.
CONCLUSIONS: The proposed method was vastly superior to other comparative applications with at least 11.41% in average accuracy and at least 6.10% in average bias; average accuracy and average absolute bias error of comparative applications were 76.33% and 17.52%, respectively.

Entities:  

Keywords:  healthcare application; sleep efficiency; sleep monitoring; sleep quality; wavelet

Mesh:

Year:  2014        PMID: 24693921     DOI: 10.1089/tmj.2013.0216

Source DB:  PubMed          Journal:  Telemed J E Health        ISSN: 1530-5627            Impact factor:   3.536


  6 in total

1.  Acoustic analysis of snoring sounds recorded with a smartphone according to obstruction site in OSAS patients.

Authors:  Soo Kweon Koo; Soon Bok Kwon; Yang Jae Kim; J I Seung Moon; Young Jun Kim; Sung Hoon Jung
Journal:  Eur Arch Otorhinolaryngol       Date:  2016-10-05       Impact factor: 2.503

2.  Unconstrained snoring detection using a smartphone during ordinary sleep.

Authors:  Hangsik Shin; Jaegeol Cho
Journal:  Biomed Eng Online       Date:  2014-08-15       Impact factor: 2.819

3.  Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring.

Authors:  N Hernandez; L Castro; J Medina-Quero; J Favela; L Michan; W Ben Mortenson
Journal:  J Healthc Inform Res       Date:  2021-02-01

4.  Long-term benefits of a new oral appliance on adult snoring: a trend analysis.

Authors:  Jui-Kun Chiang; Yen-Chang Lin; Hsiao-Chen Yu; Chih-Ming Lu; Yee-Hsin Kao
Journal:  Multidiscip Respir Med       Date:  2022-03-15

Review 5.  Challenges and Emerging Technologies within the Field of Pediatric Actigraphy.

Authors:  Barbara Galland; Kim Meredith-Jones; Philip Terrill; Rachael Taylor
Journal:  Front Psychiatry       Date:  2014-08-21       Impact factor: 4.157

6.  Validation of snoring detection using a smartphone app.

Authors:  Jui-Kun Chiang; Yen-Chang Lin; Chih-Wen Lin; Ching-Shiung Ting; Yi-Ying Chiang; Yee-Hsin Kao
Journal:  Sleep Breath       Date:  2021-04-03       Impact factor: 2.816

  6 in total

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