Seizure Prediction Using Undulated Global and Local Features

Abstract

In this study, a seizure prediction method is proposed based on a patient-specific approach by extracting undulated global and local features of preictal/ictal and interictal periods of EEG signals. The proposed method consists of feature extraction, classification, and regularization. The undulated global feature is extracted using phase correlation between two consecutive epochs of EEG signals and an undulated local feature is extracted using the fluctuation and deviation of EEG signals within the epoch. These features are further used for classification of preictal/ictal and interictal EEG signals. A regularization technique is applied on the classified outputs for the reduction of false alarms and improvement of the overall prediction accuracy (PA). The experimental results confirm that the proposed method provides high PA (i.e., 95.4%) with low false positive per hour using intracranial EEG signals in different brain locations of 21 patients from a benchmark dataset. Combining global and local features enables the transition point to be determined between different types of signals with greater accuracy, resulting successful versus unsuccessful prediction of seizure. The theoretical contribution of this study may provide an opportunity for the development of a clinical device to predict forthcoming seizure in real time.

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

“…It is nontrivial to note that the SPH was implicitly set to zero, which means prediction at a time close to or at seizure onset can be counted as a successful prediction. Likewise, research conducted by Zhang & Parhi (2016) and Parvez & Paul (2017) also implied the use of zero SPH, which will not be compared directly with our results. Among the rest of the studies listed in Table 5, Eftekhar et al (2014) had a very good prediction sensitivity of 90.95%…”

“…A lightweight approach based on spike rate achieved 75.8% sensitivity and FPR of 0.09/h (Li et al, 2013). By use of the synchronization information, a method based on phase-match error of two consecutive epochs and variation within each epoch resulted in 95.4% sensitivity and FPR of 0.36/h (Parvez & Paul, 2017).…”

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

“…Generally, for signal classification, more than one signal is required, because every movement is originated from different parts of the muscle and depends on a number of different muscles; therefore, the use of different channels helps to extract as much information as possible from the action(s) performed by the muscle(s). Among the various studies that have been done, it is common to work with four [1,9,13,23,29,38,39], six [19,40,41], or eight [2,7,11,22,30] channels for the acquisition of the signal; some research papers even work with a smaller number of channels [26,42]. Table 3 depicts an abridgement of the number of channels used by different studies and Table 4 summarizes the electrode type used and the place of electrode placement body.…”

“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”

Principle components analysis for seizures prediction using wavelet transform

“…CNN provides maximum classification accuracy and true positive rate. Parvez and Paul (2017) used phase correlation for feature extraction and least square support vector machine has been used for classification. U of v classification method has been applied as post processing for removing artifacts and to avoid misclassification.…”