Sleep is just as important as diet and exercise. Humans spend
about one third of their lives asleep.
Sleep tests involves processing and analysis of many
signals combination called as
Polysomnographic signal (PSG). In the large data sets
like Sleep Electroencephalogram (Sleep EEG), to
do analysis it becomes tedious and time taken. Instead of considering the whole
data, considering a few critical features from
the signal makes the analysis simpler and the memory requirements are also less, since the analysis could be carried out on digital
platform. A feature is a distinguishable sectional
property obtained from a portion of signal. Feature extraction depicts the
number of feature to be extracted from the
signal. Thus the feature extraction plays a pivotal role in the analysis of
Sleep EEG. In this work we discussed the
decomposition of Sleep EEG signal into required frequency bands and adopted feature extraction techniques of wavelet
decomposition method to extract features from Sleep
EEG signal by considering single channel EEG.
Author (s)
Details
Vijayakumar Gurrala
Department
of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of
Engineering and Technology, Hyderabad, Telangana, India.
Padmasai Yarlagadda
Department
of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of
Engineering and Technology, Hyderabad, Telangana, India.
Padmaraju Koppireddi
Department of Electronics and Communication Engineering, JNTU
Kakinada, Kakinada, Andhra Pradesh, India.
View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/236
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