An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

Sleep stage screening based on visual inspection is burdensome, time-consuming, subjective, and error prone owing to the large bulk of data which have to be screened. Consequently, automatic sleep scoring is essential for both sleep research and sleep disorder diagnosis. In this work, we present the application of newly proposed tunable-Q factor wavelet transform (TQWT) to devise a single channel EEG based computerized sleep staging algorithm. First, we decompose the sleep-EEG signal segments into TQWT sub-bands. Then we perform normal inverse Gaussian (NIG) pdf modeling of TQWT sub-bands wherein NIG parameters are used as features. The effects of various TQWT parameters are also studied. The suitability of NIG parameters in the TQWT domain is inspected. In this study, we employ adaptive boosting (AdaBoost) for sleep stage classification. To assess the performance of the classification model and to determine the optimal choices of AdaBoost parameters, 10 fold cross-validation is performed. The performance of the proposed scheme is promising in terms of sensitivity, specificity, accuracy, and Co-hen's Kappa co-efficient. Comparative analysis of performance suggests that the algorithmic performance of the proposed scheme, as opposed to that of the state-of-the-art ones is better. Further, the proposed algorithm also gives superior Si and REM stage detection accuracy. The computerized sleep scoring scheme propounded herein can expedite sleep disorder diagnosis, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. (C) 2016 Elsevier B.V. All rights reserved.