طبقه بندی ثبت های EEG صرع ۵-S با استفاده از آنتروپی توزیع و آنتروپی نمونه Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy

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). Epilepsy occurs in people of all ages and can affect them economically, socially, and even culturally. People with epilepsy often experience reduced 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). Recent studies show that up to 70% of people with epilepsy can be successfully treated. However, 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). Some common reasons of epilepsy are an abnormality in brain connections, an increased synchronization of neuronal activity in the brain (in which some brain cells either overexcite or over-inhibit other cells), a brain damage associated with conditions that disrupt normal brain activity, or some combination of these factors (NINDS, 2015). The hallmark of epilepsy is recurrent and unprovoked seizures. During the “epileptogenesis” process, the normal neuronal network abruptly turns into a hyper-excitable network, affecting mostly the cerebral cortex (Acharya et al., 2013). The most commonly used diagnostic tests for epilepsy is the measurement of electrical activity in the brain through monitoring electroencephalogram (EEG) or magnetoencephalogram (MEG) signals, and brain scans including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) (NINDS, 2015; Zhang et al., 2015). EEG is a non-invasive, low-cost, yet effective technique for examining the electrical activity of the brain. Abnormal spike discharges can be identified in EEG recordings before and during a seizure attack (interictal and ictal states). Recognition of interical and ictal seizure phases through the analysis of EEG features has long been studied (Acharya et al., 2013). Those EEG features are selected from a wide spectrum, including timedomain (Meier et al., 2008; Minasyan et al., 2010), frequencydomain (Polat and Güne¸s, 2007; Chua et al., 2011), timefrequency analysis (Ocak, 2009; Tzallas et al., 2009; Guo et al., 2010; Alam and Bhuiyan, 2013; Kumar et al., 2014a,b), and features based on non-linear dynamics of the signal (Yuan et al., 2011). Non-linear methods have attracted increasing attention recently, since EEG signals are considered outputs of an intrinsically non-linear, complex system—the brain. Published studies have explored the availability of different nonlinear methods, especially entropy features such as approximate entropy (ApEn) (Srinivasan et al., 2007; Ocak, 2009; Guo et al., 2010; Kumar et al., 2014a,b), sample entropy (SampEn) (Song et al., 2012), fuzzy entropy (FuzzyEn) (Kumar et al., 2014a; Xiang et al., 2015), and permutation entropy (PE) (Nicolaou and Georgiou, 2012; Li et al., 2014), or the combinations of two or more of these entropy features (Kannathal et al., 2005; Yuan et al., 2011; Acharya et al., 2012, 2015), all of which have shown good performance for distinguishing interictal, ictal EEG signals, and normal signals. However, these studies are based on the entire EEG recordings (whose lengths is usually more than 20 s) from a specific database (Srinivasan et al., 2007; Ocak, 2009; Guo et al., 2010; Yuan et al., 2011; Acharya et al., 2012; Nicolaou and Georgiou, 2012; Song et al., 2012; Kumar et al., 2014a,b). The reason for using longer data length may partly be due to the fact that the traditional entropy methods are parameter dependent and typically can achieve stable estimations only for relatively long data recordings (e.g., 1000 sampling points or more; Richman and Moorman, 2000; Xie et al., 2008; Chen et al., 2009; Yentes et al., 2013). Therefore, most of the existing algorithms are only suitable for offline or post event detection of seizure rather than online or during event detection. This limits the caregivers to take prompt action during an event, which is important for better health outcome of epileptic patient. Patients may be exposed to life-threatening conditions if a seizure onset cannot be detected promptly. Additionally, online epilepsy and seizure detection based on short-length EEG recordings is set to become increasingly favored with the emergence of portable EEG amplifiers. To the best of our knowledge, there is currently no published study that has systematically attempted to achieve accurate detection using short-length EEG recordings. In 2015, Li et al. (2015a) developed a new entropy method— distribution entropy (DistEn)—based on the distribution of inter-vector distances in the state space representation of time-series. DistEn has shown superiority for analysis of both benchmark data (Li et al., 2015a; Udhayakumar et al., 2015) and real world clinical data (Li et al., 2015) with extremely small number of samples compared with SampEn and FuzzyEn. In addition, DistEn precludes the dependence upon input parameters of the traditional methods (Li et al., 2015a; Udhayakumar et al., 2015). In our previous study, we applied this novel DistEn method to analyzing normal, interictal, and ictal EEGs, and found significant differences between the interical and ictal EEGs (Li et al., 2015b). Additionally, in that study we have explored how the length of EEGs influences DistEn performance. We tested the between group differences of DistEn using complete recording (4097 samples), average of each 5 s (868 samples) and 1 s (174 samples) epochs over complete recording, respectively. Intriguingly, we found that the performance of average DistEn of 5 s epochs was almost the same as that was found using the complete EEG recording. On the other hand, when using 1 s epochs, DistEn was not only able to detect the difference between interical and ictal EEGs, but also the difference between normal and interictal EEGs. Although the study used 5 and 1 s epoch, it is not true case of short length application, since it was averaged over the complete recording. Therefore, in the current study we will use only one epoch instead. We have decided to use epoch length of 5 s rather than 1 s in this study as we believe that 1 s epoch is too short and other algorithms (such as SampEn) mostly cannot give in valid results. One important aspect of using a short-length segments from long recording is the selection process of the segment of interest from the recording. Most studies follow the random selection procedure, which presents difficulties with regard to evaluating the reproducibility and generalizability of the technique. Since the choice of data segment mostly affects the feature and hence the overall results, the reliability of the findings becomes questionable. To address this problem, we proposed three segmentation protocols and evaluated results for each of them separately (Li et al., 2015b). In this study, we compared the performance of the DistEn and SampEn methods for classifying short-length epileptic EEG recordings with a data length of 5 s. Figure 1 shows a block diagram of this study. At first, we collected EEG signals from healthy and epileptic subjects from online database and proposed three protocols for the selection of 5 s EEG signal from complete recording. Then we used the DistEn and SampEn for feature extraction. Finally, we evaluated and compared the classification performance of extracted features among Normal, Interictal, and Ictal groups. Algorithms of DistEn and SampEn are described in Section Algorithms of DistEn and SampEn. Section Description of EEG Data summarizes the EEG data used in this study. Statistical analysis methods are provided in Section Statistical Analysis. Results are provided in Section Results, followed by discussions in Section Discussion. Conclusions are presented in the last Section.