Abstract

The absence of an accurate sleep spindle detector has hindered sleep researchers in understanding its role in sleep and its effect on the human body. In this paper, sleep spindles, marked by a neurologist, have been analyzed to develop a better understanding of its characteristic features and enable the development of a robust and reliable sleep spindle detector. As sleep spindles are characterized by high periodicity features useful in differentiating between periodic and non-periodic signals were investigated. The highest discrimination was found to be provided by the zero-crossing rate, mean peak to valley distance, autocorrelation coefficient and wavelet packet energy ratio.

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