LAUSR

Electroencephalogram (EEG) signals can be used by a proficient neurologist to detect the presence of seizure activity inside the brain. Automated detection of seizures in EEG signals has clinical importance given that manual round-the-clock monitoring of EEG signals is impossible. A patient-independent algorithm for seizure detection is developed using features extracted from high-resolution time–frequency distributions (TFDs). In order to achieve good classification performance, a modified highly adaptive time–frequency distribution (HADTFD) is defined. The modified-HADTFD is used to obtain a clear and cross-term free time–frequency representation of EEG signals. This is followed by the extraction of features and training of a linear classifier. The proposed approach based on modified-HADTFD achieves the classification accuracy of $$98.56\%$$98.56% by using only three time–frequency features, which is $$37\%$$37% more than the accuracy achieved with other TFDs.