EEG Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis

Electroencephalogram (EEG) signals resulting from recordings of polysomnography play a significant role in determining the changes in physiology and behavior during sleep. This study aims at demarcating the sleep patterns of yogic and non-yogic subjects. Frequency domain features based on power spectral density methods were explored in this study. The EEG recordings were segmented into 1s and 0.5s. EEG patterns with four windowing scheme overlaps (0%, 50%, 60% and 75%) to ensure stationarity of the signal in order to investigate the effect of the pre-processing stage. In order to recognize the yoga and non-yoga group through N3 sleep stage, non-linear KNN classifier was introduced and performance was evaluated in terms of sensitivity and specificity. The experimental results show that modified covariance PSD estimate is the best method in classifying the sleep stage N3 of yogic and non-yogic subjects with 95% confidence interval, sensitivity, specificity and accuracy of 97.3%, 98% and 97%, respectively.

Download Full-text

Period-amplitude analysis and power spectral analysis: a comparison based on all-night sleep EEG recordings

Journal of Sleep Research ◽

10.1111/j.1365-2869.1993.tb00074.x ◽

1993 ◽

Vol 2 (3) ◽

pp. 121-129 ◽

Cited By ~ 34

Author(s):

BEAT A. GEERING ◽

PETER ACHERMANN ◽

FRITZ EGGIMANN ◽

ALEXANDER A. BORBÉLY

Keyword(s):

Spectral Analysis ◽

Power Spectral Analysis ◽

Sleep Eeg ◽

Night Sleep ◽

Amplitude Analysis ◽

Power Spectral ◽

Eeg Recordings

Download Full-text

Night sleep electroencephalogram power spectral analysis in excessive daytime sleepiness disorders

Arquivos de Neuro-Psiquiatria ◽

10.1590/s0004-282x1991000200002 ◽

1991 ◽

Vol 49 (2) ◽

pp. 128-135 ◽

Cited By ~ 1

Author(s):

Rubens Reimão

Keyword(s):

Spectral Analysis ◽

Affective Disorder ◽

Excessive Daytime Sleepiness ◽

Daytime Sleepiness ◽

Sleep Stage ◽

Power Spectral Analysis ◽

Frequency Bands ◽

Power Spectral ◽

Stage 4 ◽

Stage 1

A group of 53 patients (40 míales, 13 females) with mean age of 49 years, ranging from 30 to 70 years, was evaluated in the. following excessive daytime sleepiness (EDS) disorders : obstructive sleep apnea syndrome (B4a), periodic movements in sleep (B5a), affective disorder (B2a), functional psychiatric non affective disorder (B2b). We considered all adult patients referred to the Center sequentially with no other distinctions but these three criteria: (a) EDS was the main complaint; (b) right handed ; (c) not using psychotropic drugs for two weeks prior to the all-night polysomnography. EEG (C3/A1, C4/A2) samples from 2 to 10 minutes of each stage of the first REM cycle were chosen. The data was recorded simultaneously in magnetic tape and then fed into a computer for power spectral analysis. The percentage of power (PP) in each band calculated in relation to the total EEG power was determined of subsequent sections of 20.4 s for the following frequency bands: delta, theta, alpha and beta. The PP in all EOS patients sample had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage. PP distribution in the delta range increased progressively from stage 1 to stage 4; stage REM levels were close to stage 2 levels. In an EDS patients interhemispheric coherence was high in every band and sleep stage. B4a patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in¡ every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 4; stage REM levels were between stage 1 and stage 2 levels. B2a patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 4; stage REM levels were close to stage 2 levels. B2b patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 3; stage 4 levels were close to stage 3 levels; stage REM levels were close to stage 2. B5a patients sample PP had a tendency to decrease progressively from the slowest to the fastest frequency bands, in every sleep stage; PP distribution in the delta range increased progressively from stage 1 to stage 3; stage REM levels were close to stage 2 levels, Interhemispheric coherences of B4a, B2b, and B5a groups were high in, every band and sleep stage. B4a, B2a, B2b, and B5a power spectral analysis comparison showed higher PP in B2b stage 1 alpha band, as well as, higher PP in B5a stage 2 theta band. The B4a versus. B2a power spectral analysis comparison showed higher PP in B4a stages 1 and REM alpha bands, as well as higher PP in B2a stage REM delta band.

Download Full-text

Spectral Analysis of the EEG in Craniocerebral Trauma

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/s0317167100027244 ◽

1988 ◽

Vol 15 (1) ◽

pp. 82-86 ◽

Cited By ~ 15

Author(s):

Richard J. Moulton ◽

Anthony Marmarou ◽

Jacob Ronen ◽

John D. Ward ◽

Sung Choi ◽

...

Keyword(s):

Spectral Analysis ◽

Craniocerebral Trauma ◽

Power Spectral Analysis ◽

Post Injury ◽

Term Outcome ◽

Long Term Outcome ◽

Linear Discriminant ◽

Power Spectral ◽

Number Of Patients ◽

Eeg Recordings

ABSTRACT:The objectives of the present study were to evaluate the relationship between the fractional amplitudes of the EEG derived from power spectral analysis (PSA) of the electroencephalogram (EEG) and depth of coma measured clinically with the Glasgow Coma Score, and to assess the accuracy of PSA in predicting long-term outcome. Thirty-two patients rendered unconscious by blunt head injury (mean (GCS = 7) had intermittent EEG recordings daily from 1-10 days post injury. There was a significant correlation between fractional amplitude of the EEG and the GCS. The rate and magnitude of change in the EEG and GCS were also correlated. There were significant differences in PSA parameters between improved and deteriorated patient groups at the termination of monitoring (p = .02) and in the change of PSA parameters over time (p = .02). Using linear discriminant analysis of PSA parameters, the accuracy of outcome prognostication based on the six month outcome was approximately 75%. Accurate classification of outcome was possible in a number of patients in whom there was little or no change in the GCS during the period of monitoring.

Download Full-text

Effects of acute clonidine administration on power spectral analysis of heart rate variability in healthy humans

Journal of Autonomic Pharmacology ◽

10.1046/j.1365-2680.1998.18599.x ◽

1998 ◽

Vol 18 (5) ◽

pp. 307-312 ◽

Cited By ~ 14

Author(s):

C. Lazzeri ◽

G. La Villa ◽

M. Mannelli ◽

L. Janni ◽

F. Franchi

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Spectral Analysis ◽

Power Spectral Analysis ◽

Healthy Humans ◽

Power Spectral

Download Full-text

Altered Brain Function as Evidenced by Electroencephalographic Power Spectral Analysis in Patients with Opioid Dependence

PsycEXTRA Dataset ◽

10.1037/e575152014-001 ◽

2014 ◽

Author(s):

Vijay Mehtrya ◽

Nizamuddin Parvezb ◽

Haque Nizamieb ◽

Nityananda Pradhanc

Keyword(s):

Spectral Analysis ◽

Brain Function ◽

Opioid Dependence ◽

Power Spectral Analysis ◽

Power Spectral

Download Full-text

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

Methods of Information in Medicine ◽

10.1055/s-0038-1636853 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 41-46

Author(s):

A. Kjaer ◽

W. Jensen ◽

T. Dyrby ◽

L. Andreasen ◽

J. Andersen ◽

...

Keyword(s):

Sleep Stage ◽

Spectral Estimation ◽

Interrater Agreement ◽

Probabilistic Networks ◽

Probabilistic Network ◽

Spectral Bands ◽

Power Spectral ◽

Stage Classification ◽

Sleep Stage Classification ◽

The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

Download Full-text