Next Article in Journal
Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing
Next Article in Special Issue
Modified Hermite Pulse-Based Wideband Communication for High-Speed Data Transfer in Wireless Sensor Applications
Previous Article in Journal / Special Issue
A Low-Power Active Self-Interference Cancellation Technique for SAW-Less FDD and Full-Duplex Receivers
Article Menu

Export Article

Open AccessArticle
J. Low Power Electron. Appl. 2017, 7(4), 28; https://doi.org/10.3390/jlpea7040028

Sleep Stage Classification by a Combination of Actigraphic and Heart Rate Signals

1
Graduate School of Medical Sciences, Nagoya City University, Nagoya 467-8602, Japan
2
Sleep Disorder Center, Aichi Medical University Hospital, Nagakute 480-1195, Japan
3
Gifu Mates Sleep Clinic, Gifu 500-8384, Japan
*
Author to whom correspondence should be addressed.
Received: 25 September 2017 / Revised: 9 November 2017 / Accepted: 9 November 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Low-Power Electronic Circuits for Monolithic Smart Wireless Sensors)
View Full-Text   |   Download PDF [978 KB, uploaded 13 November 2017]   |  

Abstract

Although heart rate variability and actigraphic data have been used for sleep-wake or sleep stage classifications, there are few studies on the combined use of them. Recent wearable sensors, however, equip both pulse wave and actigraphic sensors. This paper presents results on the performance of sleep stage classification by a combination of heart rate and actigraphic signals. We studied 40,643 epochs (length 3 min) of polysomnographic data in 289 subjects. A combined model, consisting of autonomic functional indices from heart rate variability and body movement indices derived from actigraphic data, discriminated non-rapid-eye-movement (REM) sleep from waking/REM sleep with 76.9% sensitivity, 74.5% specificity, 75.8% accuracy, and a Cohen’s kappa of 0.514. The combination was also useful for discriminating between REM sleep and waking at 77.2% sensitivity, 72.3% specificity, 74.5% accuracy, and a kappa of 0.491. View Full-Text
Keywords: actigraphy; heart rate variability; polysomnography; sleep; sleep stage actigraphy; heart rate variability; polysomnography; sleep; sleep stage
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Yuda, E.; Yoshida, Y.; Sasanabe, R.; Tanaka, H.; Shiomi, T.; Hayano, J. Sleep Stage Classification by a Combination of Actigraphic and Heart Rate Signals. J. Low Power Electron. Appl. 2017, 7, 28.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Low Power Electron. Appl. EISSN 2079-9268 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top