TY - GEN
T1 - Hurst exponents based detection of wake-sleep - A pilot study
AU - Sriraam, N.
AU - Purnima, B. R.
AU - Uma, K.
AU - Padmashri, T. K.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/10
Y1 - 2014/3/10
N2 - Detection of Sleep onset is one of complex processes in the area of sleep medicine. The transition from wake state to sleep is termed as sleep onset and is identified using distinct markers like behavioural features, physiological features and changes in EEC Extraction of appropriate features from EEG recordings helps in automated recognition and classification of wake-sleep transition. This research study proposes the introduction of Hurst exponent (HE) to indicate the transition between wake and sleep derived from EEG recordings. Being the non-linear chaotic parameter, Hurst exponent quantifies correlation among the time series data and this property has been exploited for sleep EEGs. Two typical channels O1 and O2 were used for the study and Hurst exponents were estimated for the EEG segments followed by classification using two linear classifiers, LDA and KNN. The statistical analysis confirms that the mean value of HE is lower for sleep than wake. The preliminary study reveals a classification accuracy of 99.96% with HE features with KNN classifier. The procedure needs to be tested with larger datasets.
AB - Detection of Sleep onset is one of complex processes in the area of sleep medicine. The transition from wake state to sleep is termed as sleep onset and is identified using distinct markers like behavioural features, physiological features and changes in EEC Extraction of appropriate features from EEG recordings helps in automated recognition and classification of wake-sleep transition. This research study proposes the introduction of Hurst exponent (HE) to indicate the transition between wake and sleep derived from EEG recordings. Being the non-linear chaotic parameter, Hurst exponent quantifies correlation among the time series data and this property has been exploited for sleep EEGs. Two typical channels O1 and O2 were used for the study and Hurst exponents were estimated for the EEG segments followed by classification using two linear classifiers, LDA and KNN. The statistical analysis confirms that the mean value of HE is lower for sleep than wake. The preliminary study reveals a classification accuracy of 99.96% with HE features with KNN classifier. The procedure needs to be tested with larger datasets.
UR - https://www.scopus.com/pages/publications/84946689595
UR - https://www.scopus.com/pages/publications/84946689595#tab=citedBy
U2 - 10.1109/CIMCA.2014.7057771
DO - 10.1109/CIMCA.2014.7057771
M3 - Conference contribution
AN - SCOPUS:84946689595
T3 - Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014
SP - 118
EP - 121
BT - Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014
Y2 - 21 November 2014 through 22 November 2014
ER -