Enhanced automated sleep spindle detection algorithm based on synchrosqueezing.

Detection of sleep spindles is of major importance in the field of sleep research. However, manual scoring of spindles on prolonged recordings is very laborious and time-consuming. In this paper, we introduce a new algorithm based on synchrosqueezing transform for detection of sleep spindles. Synchrosqueezing is a powerful time-frequency analysis tool that provides precise frequency representation of a multicomponent signal through mode decomposition. Subsequently, the proposed algorithm extracts and compares the basic features of a spindle-like activity with its surrounding, thus adapting to an expert's visual criteria for spindle scoring. The performance of the algorithm was assessed against the spindle scoring of one expert on continuous electroencephalogram sleep recordings from two subjects. Through appropriate choice of synchrosqueezing parameters, our proposed algorithm obtained a maximum sensitivity of 96.5% with 98.1% specificity. Compared to previously published works, our algorithm has shown improved performance by enhancing the quality of sleep spindle detection.