Abstract
Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure—sample entropy (SampEn)—and a more recently proposed complexity measure—distribution entropy (DistEn)—were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.
Highlights
Epilepsy is the fourth most common neurological disorder after migraine, stroke, and Alzheimer’s disease (Sirven and Shafer, 2014) with an estimated 50 million people globally living with epilepsy (Media-Center, 2015)
sample entropy (SampEn) indices remained undefined for all three segmentation protocols (Table 2) and both distribution entropy (DistEn) and SampEn showed similar results over three protocols as indicated by Tables 1, 2
Difference between interictal and ictal classes indicated by SampEn indices are statistically insignificant
Summary
Epilepsy is the fourth most common neurological disorder after migraine, stroke, and Alzheimer’s disease (Sirven and Shafer, 2014) with an estimated 50 million people globally living with epilepsy (Media-Center, 2015). DistEn and SampEn for Epilepsy Analysis educational opportunities, barriers to particular occupations, reduced access to health and life insurance, and other social stigma and discrimination (Sirven and Shafer, 2014; MediaCenter, 2015). About three fourths in low- and middle-income countries may not receive the treatment they need. This is a considerable “treatment gap,” since nearly 80% of the epilepsy population live in those countries (Media-Center, 2015). Barriers to treatment for those people include the lack of trained healthcare providers and reliable low-cost diagnostic techniques (Media-Center, 2015)
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