Abstract: Sleep spindles are the most interesting hallmark of
stage 2 sleep EEG. Their accurate identification in a
polysomnographic signal is essential for sleep professionals to help
them mark Stage 2 sleep. Sleep Spindles are also promising objective
indicators for neurodegenerative disorders. Visual spindle scoring
however is a tedious workload. In this paper three different
approaches are used for the automatic detection of sleep spindles:
Short Time Fourier Transform, Wavelet Transform and Wave
Morphology for Spindle Detection. In order to improve the results, a
combination of the three detectors is presented and comparison with
human expert scorers is performed. The best performance is obtained
with a combination of the three algorithms which resulted in a
sensitivity and specificity of 94% when compared to human expert
scorers.
Abstract: One of the most important causes of accidents is
driver fatigue. To reduce the accidental rate, the driver needs a
quick nap when feeling sleepy. Hence, searching for the minimum
time period of nap is a very challenging problem. The purpose of
this paper is twofold, i.e. to investigate the possible fastest time
period for nap and its relationship with stage 2 sleep, and to
develop an automatic stage 2 sleep detection and alarm device. The
experiment for this investigation is designed with 21 subjects. It
yields the result that waking up the subjects after getting into stage
2 sleep for 3-5 minutes can efficiently reduce the sleepiness.
Furthermore, the automatic stage 2 sleep detection and alarm
device yields the real-time detection accuracy of approximately
85% which is comparable with the commercial sleep lab system.