Optimized Tunable Q Wavelet Transform Based Drowsiness Detection from Electroencephalogram Signals

Early discernment of drivers drowsy state may prevent numerous worldwide road accidents. Electroencephalogram (EEG) signals provide valuable information about the neurological changes for discrimination of alert and drowsy state. A signal is decomposed into multi-components for the analysis of the physiological state. Tunable Q wavelet transform (TQWT) decomposes the signal into low-pass and high-pass sub-bands without a choice of wavelet. The information content captured by these sub-bands depends on the choice of decomposition parameters. Due to the non-stationary nature of EEG signals, the predefined decomposition parameters of TQWT lead to information loss and degrade system performance. Hence it is required to automate the decomposition parameters in accordance with the nature of signals. In this paper, an optimized tunable Q wavelet transform (O-TQWT) is proposed for the adaptive selection of decomposition parameters by using different optimization algorithms. Objective function as a mean square error (MSE) of decomposition is minimized by optimization algorithms. Optimum decomposition parameters are used to decompose the signals into sub-bands. Time-domain based features are excerpted from the sub-bands of O-TQWT. Highly discriminant features selected by using Kruskal Wallis test are used as an input to different classification techniques. Classification accuracy of 96.14% is achieved by least square support vector machine with radial basis function kernel which is better than the other existing methodologies using the same database.