EEG signals based automated epilepsy detection using tunable-Q wavelet transform and Hjorth parameters

In recent years, electroencephalogram (EEG) signals have been increasingly used in the automatic identification of brain diseases. Most of the current EEG-based methods for epilepsy classification focused on single dimensional feature extraction techniques in the time domain or frequency domain, respectively. In this paper,we proposed a joint time-frequency domain feature extraction method using tunable-Q wavelet transform and Hjorth parameters. The tunable-Q wavelet transform is used to decompose the EEG signal into different subbands, the optimal subbands are calculated according to the maximum energy criterion and divided into different frequency ranges, and the Hjorth parameters (activity, mobility, and complexity) are calculated on the optimal self-bands of different frequency ranges, which are applied to the machine learning algorithm to realize the automatic epilepsy detection. The experimental results show that the method has a high classification accuracy, determines the frequency range in which seizures are active and further improves the accuracy of automated seizure detection.