Epileptic Seizure Prediction Based on Permutation Entropy

Abstract

Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h^-1. The best results with SS of 100% and FPR of 0 h^-1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.

Keywords: electroencephalogram; epilepsy; permutation entropy; prediction; support vector machine (SVM).

Figure 1

Block diagram of the proposed seizure prediction method. In the third block, H_ab (1 ≤ a ≤ 6,1 ≤ b ≤ x) represent PE. The letter a corresponds to the six channels, and the letter x is the number of samples after computing PE in a 5-s sliding window per channel.

Figure 2

Prediction system. In-ic = Interictal; Pre-ic = Preictal.

Figure 3

Demonstration of classified output after SVM classification and proposed regularization using iEEG signals from the first seizure of patient 17. (A) Shows a normal situation with no alarm in two-step FP. (B) Shows the application of post-processing during the preictal period in two-step FP.

Figure 4

It represents PE for the first seizure of patient 17, in which the interval between the two blue vertical lines is ictal period. There are six different lines, each colored broken line represents changes in PE for one channel. The red triangle represents the range of PE during the interictal period.

Figure 5

SPH and average SPH for 19 patients.

Figure 6

Euclidean distance in two-step FP and one-step FP.