Insomnia Identification via Electroencephalography
Olviya Udeshika Dilshan Lakshitha Nilantha Premakumara Surangani Bandara
Insomnia is a serious sleep disorder caused by abnormal or excessive neural
activity in the brain. An estimated 50 million people worldwide are thought to
be affected by this condition, which is the second most severe neurological
disease after stroke. In order to ensure a quick recovery, an early and
accurate diagnosis of insomnia enables more effective drug and treatment
administration. This study proposes a method that uses deep learning to
automatically identify patients with insomnia. A set of optimal features are
extracted from spectral and temporal domains, including the relative power of
{\sigma}, \b{eta} and {\gamma} bands, the total power, the absolute slow wave
power, the power ratios of {\theta}, {\alpha}, {\gamma}, \b{eta},
{\theta}/{\alpha}, {\theta}/\b{eta}, {\alpha}/{\gamma} and {\alpha}/\b{eta},
mean, zero crossing rate, mobility, complexity, sleep efficiency and total
sleep time, to accurately quantify the differences between insomnia patients
and healthy subjects and develops a 1D CNN model for the classification
process. With the experiments use Fp2 and C4 EEG channels with 50 insomnia
patients and 50 healthy subjects, the proposed model arrives 99.34% accuracy
without sleep stage annotation. Using the features only from a single channel,
the study proposes a smart solution for insomnia patients which allows machine
learning to be to simplify current sleep monitoring hardware and improve
in-home ambulatory monitoring.