Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy

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.

Entropy Analysis of Short-Term Heartbeat Interval Time Series during Regular Walking

“…Those established entropy measures include approximate entropy (ApEn) [23], sample entropy (SampEn) [24], fuzzy entropy (FuzzyEn) [25], permutation entropy (PermEn) [26], conditional entropy (CE) [27], and distribution entropy (DistEn) [28], etc. Different entropy measures likely capture different dynamical properties [29]. However, to our knowledge, there are no published studies that have examined whether those entropy measures respond to exercise in the same way and which entropy measure responds to the stimuli more sensitively.…”

Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

“…Significance was set at p < .05. Entropy values have been reported to usually follow a non-normal distribution(Li, Karmakar, Yan, Palaniswami, & Liu, 2016), so we performed a normality test analysis of the calculated entropy value (p = .07). Because the p value was close to .05 and our sample size was not large, we chose Spearman's rank correlation.…”

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

“…Sample Entropy [23] was introduced for time-sequence analysis, complexity measurement in particular, which has been applied in physiological signal analysis [9]. For discrete time sequence X = {x(1), x(2), ..., x(N)}, the sample entropy can be calculated as follows:…”

“…Such automated seizure detectors can trigger alarm when users are or will possibly be in a state of seizure. So far, algorithms for automated epileptic seizure detection proposed in most studies consist of three parts: (1) signal domain transformation, such as frequency domain via Fourier transform [3], wavelet time-frequency domain via discrete wavelet transform (DWT) [4,5], weighted and specific shapes via Hermite transformation [6] or original domain without transformation [7]; (2) feature extraction in the target domain, such as energy features [8] and complexity features [9]; and (3) machine learning based classification using a support vector machine (SVM) [10], k-nearest neighbor (KNN) [11] or artificial neural network (ANN) [12]. However, all the aforementioned three parts have shown limitations in some application scenarios, which are discussed in the following paragraphs separately.…”

“…For feature extraction in the target domain, complexity features [9] have received a great deal of attention in the biomedical signal processing field in recent years. To measure the complexity property of EEG signals during epileptic seizures, entropy-based features have been widely used, such as sample entropy [9], fuzzy entropy [10], approximate entropy [14], permutation entropy [15] and distribution entropy [9]. All these entropy indicators listed above measure EEG complexity on a single scale.…”