An instantaneous frequency and group delay based feature for classifying EEG signals. (May 2021) - Khan, Nabeel Ali; Ali, Sadiq; Choi, Kwonhue
Highlights: A new feature to detect seizures in EEG recordings of newborns is proposed. The proposed feature exploits both instantaneous frequency and group delay. The proposed feature can detect both spike type and oscillatory type EEG seizures. The proposed feature in combination with other commonly used features achieve better performance than the existing methods. Abstract: The instantaneous frequency has been frequently employed as a feature for the detection of the oscillatory type of seizures in electroencephalogram signals. However, seizures appearing as spikes cannot be analyzed using the Instantaneous frequency. In this study, we propose a new time-frequency feature that exploits both the instantaneous frequency and group delay to detect seizures with both spike and oscillatory characteristics in electroencephalogram (EEG) recordings. It is demonstrated that the computational cost of extracting the proposed feature is less than the features of similar performance. To evaluate the performance of the proposed feature, we employed a newborn EEG database. The proposed feature in combination with other features achieves a classification accuracy of 99%, which is 0.75% greater than the existing methods.