A decision support system for automated identification of sleep stages from single-channel EEG signals

A single channel EEG based automated sleep scoring method is proposed.A novel signal processing technique, namely TQWT is employed for sleep staging.We introduce bagging to classify sleep stages.Efficacy of the method is confirmed by statistical and graphical analyses.The performance of the proposed scheme, compared to the existing ones is promising. A decision support system for automated detection of sleep stages can alleviate the burden of medical professionals of manually annotating a large bulk of data, expedite sleep disorder diagnosis, and benefit research. Moreover, the implementation of a sleep monitoring device that is low-power and portable requires a reliable and successful sleep stage detection scheme. This article presents a methodology for computer-aided scoring of sleep stages using singe-channel EEG signals. EEG signal segments are first decomposed into sub-bands using tunable-Q wavelet transform (TQWT). Four statistical moments are then extracted from the resulting TQWT sub-bands. The proposed scheme exploits bootstrap aggregating (Bagging) for classification. Efficacy of the feature generation scheme is evaluated using intuitive, statistical, and Fisher criteria analyses. Furthermore, the efficacy of Bagging is evaluated using out-of-bag error analysis. Optimal choices of Bagging and TQWT parameters are explicated. The proposed methodology for automated sleep scoring is tested on the benchmark Sleep-EDF database and DREAMS Subjects database. Our methodology achieves 92.43%, 93.69%, 94.36%, 96.55%, and 99.75% accuracy for 2-state to 6-state classification of sleep stages on Sleep-EDF database. Experimental results show that the algorithmic performance of the automated sleep scoring technique presented herein achieves better performance as compared to the state-of-the-art sleep staging algorithms. Besides, the proposed scheme performs equally well for two sleep scoring standards, namely AASM and R&K. Moreover, the proposed decision support system yields high success rate for identifying sleep states REM and non-REM 1. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of medical professionals of annotating a large volume of recordings manually, and expedite sleep disorder diagnosis.

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