Assessment of sleep quality in powernapping
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Assessment of sleep quality in powernapping. / Kooravand Takht Sabzy, Bashaer; Thomsen, Carsten E.
In: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Vol. 2011, 2011, p. 769-72.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Assessment of sleep quality in powernapping
AU - Kooravand Takht Sabzy, Bashaer
AU - Thomsen, Carsten E
PY - 2011
Y1 - 2011
N2 - The purpose of this study is to assess the Sleep Quality (SQ) in powernapping. The contributed factors for SQ assessment are time of Sleep Onset (SO), Sleep Length (SL), Sleep Depth (SD), and detection of sleep events (K-complex (KC) and Sleep Spindle (SS)). Data from daytime nap for 10 subjects, 2 days each, including EEG and ECG were recorded. The SD and sleep events were analyzed by applying spectral analysis. The SO time was detected by a combination of signal spectral analysis, Slow Rolling Eye Movement (SREM) detection, Heart Rate Variability (HRV) analysis and EEG segmentation using both Autocorrelation Function (ACF), and Crosscorrelation Function (CCF) methods. The EEG derivation FP1-FP2 filtered in a narrow band and used as an alternative to EOG for SREM detection. The ACF and CCF segmentation methods were also applied for detection of sleep events. The ACF method detects segment boundaries based on single channel analysis, while the CCF includes spatial variation from multiple EEG derivation. The results indicate that SREM detection using EEG is possible and can be used as input together with power spectral analysis to enhance SO detection. Both segmentation methods could detect SO as a segment boundary. Additionally they were able to contribute to detection of KC and SS events. The CCF method was more sensitive to spatial EEG changes and the exact segment boundaries varied slightly between the two methods. The HRV analysis revealed, that low and very low frequency variations in the heart rate was highly correlated with the EEG changes during both SO and variations in SD. Analyzing the relationship between the sleep events and SD showed a negative correlation between the Delta and Sigma activity. Analyzing the subjective measurement (SM) showed that there were a positive correlation between the SL and rated SQ. This preliminary study showed that the factors contributing to the overall SQ during powernapping can be assessed markedly better using a fusion of multiple methods. Future studies will include measures of individual performance before and after powernapping and investigate its relation to the assessed SQ.
AB - The purpose of this study is to assess the Sleep Quality (SQ) in powernapping. The contributed factors for SQ assessment are time of Sleep Onset (SO), Sleep Length (SL), Sleep Depth (SD), and detection of sleep events (K-complex (KC) and Sleep Spindle (SS)). Data from daytime nap for 10 subjects, 2 days each, including EEG and ECG were recorded. The SD and sleep events were analyzed by applying spectral analysis. The SO time was detected by a combination of signal spectral analysis, Slow Rolling Eye Movement (SREM) detection, Heart Rate Variability (HRV) analysis and EEG segmentation using both Autocorrelation Function (ACF), and Crosscorrelation Function (CCF) methods. The EEG derivation FP1-FP2 filtered in a narrow band and used as an alternative to EOG for SREM detection. The ACF and CCF segmentation methods were also applied for detection of sleep events. The ACF method detects segment boundaries based on single channel analysis, while the CCF includes spatial variation from multiple EEG derivation. The results indicate that SREM detection using EEG is possible and can be used as input together with power spectral analysis to enhance SO detection. Both segmentation methods could detect SO as a segment boundary. Additionally they were able to contribute to detection of KC and SS events. The CCF method was more sensitive to spatial EEG changes and the exact segment boundaries varied slightly between the two methods. The HRV analysis revealed, that low and very low frequency variations in the heart rate was highly correlated with the EEG changes during both SO and variations in SD. Analyzing the relationship between the sleep events and SD showed a negative correlation between the Delta and Sigma activity. Analyzing the subjective measurement (SM) showed that there were a positive correlation between the SL and rated SQ. This preliminary study showed that the factors contributing to the overall SQ during powernapping can be assessed markedly better using a fusion of multiple methods. Future studies will include measures of individual performance before and after powernapping and investigate its relation to the assessed SQ.
KW - Adolescent
KW - Adult
KW - Brain
KW - Female
KW - Heart Rate
KW - Humans
KW - Male
KW - Polysomnography
KW - Sleep Stages
KW - Young Adult
U2 - 10.1109/IEMBS.2011.6090176
DO - 10.1109/IEMBS.2011.6090176
M3 - Journal article
C2 - 22254424
VL - 2011
SP - 769
EP - 772
JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SN - 0589-1019
ER -
ID: 40342190