Performance of the frequency domain indices with respect to sleep staging

• Published in: Clinical neurophysiology Vol. 123; no. 7; pp. 1338 - 1345
• Main Authors: Kuo, Terry B.J., Chen, C.Y., Hsu, Ya-Chuan, Yang, Cheryl C.H.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ireland Ltd 01.07.2012; Elsevier
• Subjects: Acceleration; Adolescent; Adult; Beta Rhythm - physiology; Biological and medical sciences; Computer scoring; Electrodiagnosis. Electric activity recording; Electroencephalography; Electromyography; Electrophysiological Phenomena - physiology; Female; Frequency domain analysis; Fundamental and applied biological sciences. Psychology; Heart Rate - physiology; Heart rate variability; Humans; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Nervous system; Neurology; Sleep - physiology; Sleep Stages - physiology; Sleep staging; Sleep, REM - physiology; Sleep. Vigilance; Theta Rhythm - physiology; Vertebrates: nervous system and sense organs; Wakefulness - physiology; Young Adult; Computer scoring; Acceleration; Heart rate variability; Frequency domain analysis; Sleep staging; Human; Sleep stage; Algorithm; Eye movement; Slow wave sleep; Spectral analysis; Heart rate; Wakefulness; Computer; Sleep wake cycle; High frequency; Performance; Signal analysis
• ISSN: 1388-2457; 1872-8952; 1872-8952
• DOI: 10.1016/j.clinph.2011.11.003

To compare computerized staging using spectral analyses of various electrophysiological signals with manual sleep staging. Sleep recordings from 21 normal subjects were scored by an experienced rater and by a dichotomous algorithm. The performance of the spectral indices was assessed by the largest kappa value (LKV). Theta/beta power ratio of the electroencephalogram, high frequency power (8–58Hz) of the electromyogram (PEMG), mean R–R interval, and total power (0–16Hz) of the body acceleration (PACCE) had high (>0.5) LKVs when differentiating between waking and sleep. To differentiate sleep with (stage 2 and slow wave sleep) and without (rapid eye movement and stage 1 sleep) spindles, sigma/beta power ratio had high LKVs. PEMG had a medium (>0.25) LKV to separate rapid eye movement from stage 1 sleep whereas delta/beta power ratio had a high LKV to separate stage 2 and slow wave sleep. The frequency components of electroencephalogram perform well in identifying sleep, sleep with spindles, and slow wave sleep. Electromyogram, heart rate, and body acceleration offer high agreement only when differentiating between wakefulness and sleep. The human–machine agreement is acceptable with spectral parameters, but heart rate and body acceleration still cannot substitute for electroencephalogram.