A decision support system for automatic sleep staging from EEG signals using tunable Q factor wavelet transform and spectral features
BACKGROUND Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time consuming and error prone. Therefore, there is a dire need of an automated sleep staging scheme. NEW METHOD In this work, we decompose sleep EEG signal segments using tunable Q factor wavelet transform (TQWT) Various spectral features are then computed from TQWT sub bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded. RESULTS Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38% 91.50% 92.11% 94.80% 97.50% for 6 stage to 2 stage classification of sleep states on the benchmark Sleep EDF data set. In addition, its performance on DREAMS Subjects Data set is also promising. COMPARISON WITH EXISTING METHODS The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non REM 1 and REM. CONCLUSIONS Spectral features in the TQWT domain can discriminate sleep EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed up sleep disorder diagnosis, and expedite sleep research.
Journal of Neuroscience Methods