Automatic epileptic seizure detection in a mixed generalized and focal seizure dataset

Detection of seizure periods in an epileptic patient is an important part of health care. However, due to the variety in types of seizures and location of them, real-time seizure detection is not straight forward. In this paper, we propose a method for seizure detection from EEG signals in datasets which have both generalized and focal seizures. The proposed method is useful in the situations that we have no prior knowledge about the location of the patient's seizure and the pattern of evolution of seizure location. In the proposed method, first, the artifacts are automatically reduced by Blind Source Separation (BSS) methods. Then, the channels are clustered into two clusters. After that, the channels of each cluster are classified into the seizure and non-seizure groups. The final decision is made based on voting in each cluster. If at least one cluster shows seizure behavior, we assign the corresponding epoch to seizure class. Results show that our method achieved 80.72% accuracy, 80% sensitivity, 81.08% specificity, and 67.55% precision in a mixed generalized and focal seizure dataset.