Abbas Muhammad Khizar, Raza Muhammad Liaquat, Zaidi Syed Sajjad Haider, Khan Bilal Muhammad, Heinemann Uwe
a Electronics & Power Engineering, Pakistan Navy Engineering College, Karachi , National University of Sciences and Technology , Islamabad , Pakistan.
b Department of Pharmacology, Pharmacy , Hamdard University , Karachi , Pakistan.
Neurol Res. 2019 Feb;41(2):99-109. doi: 10.1080/01616412.2018.1532481. Epub 2018 Oct 17.
Epilepsy is a neurological disorder affecting 50 million individuals globally. Modern research has inspected the likelihood of forecasting epileptic seizures. Algorithmic investigations are giving promising results for seizure prediction. Though mostly seizure prediction algorithm uses pre-ictal (prodromal symptoms) events for prediction. On the contrary, prodromal symptoms may not necessarily be present in every patient or subject. This paper focuses on seizure forecasting regardless of the presence of pre-ictal (prodromal symptoms) using the single robust feature with maximum accuracy. Method: We evaluated datasets having 4-aminopydine induced seizure-like events rat's hippocampa slices and cortical tissue from pharmacoresistant epilepsy patients. The proposed methodology applies the Discrete Wavelet Transform (DWT) at levels 1-5 utilizing 'Daubechies-4'. Linear Discriminant classifier (LDC), Quadratic Discriminant Classifier (QDC) and Support Vector Machine (SVM) were used to classify each signal using eight discriminative features. Results: Classifier performance was assessed by parameters like true detections (TD), false detection (FD), accuracy (ACC), sensitivity (SEN), specificity (SPF), and positive predicted value (PPC), negative predicted value (NPV). Highest classification feature was selected as a seizure forecasting correlation vector and decision rule was formulated for seizure forecasting. Correlation vector served as a forecaster for current EEG activity. Proposed decision rule forecasted ongoing signal activity towards possible seizure condition true or false. The suggested framework revealed forecasting of ictal events at 10 seconds before the actual seizure. Conclusion: It is worth mentioning that the proposed study utilized a single linear feature to predict seizures precisely. Moreover, utilization of single feature encouraged in subsiding system complexity, processing delays, and system latency.
癫痫是一种影响全球5000万人的神经系统疾病。现代研究已经考察了预测癫痫发作的可能性。算法研究在癫痫发作预测方面给出了很有前景的结果。尽管大多数癫痫发作预测算法使用发作前（前驱症状）事件进行预测。相反，前驱症状不一定在每个患者或个体中都出现。本文聚焦于无论是否存在发作前（前驱症状），都使用单一强大特征以最高准确率进行癫痫发作预测。方法：我们评估了包含4-氨基吡啶诱发的癫痫样事件大鼠海马切片和耐药性癫痫患者皮质组织的数据集。所提出的方法在1-5级应用离散小波变换（DWT），使用“Daubechies-4”小波。使用线性判别分类器（LDC）、二次判别分类器（QDC）和支持向量机（SVM），利用八个判别特征对每个信号进行分类。结果：通过真检测（TD）、假检测（FD）、准确率（ACC）、灵敏度（SEN）、特异性（SPF）、阳性预测值（PPC）、阴性预测值（NPV）等参数评估分类器性能。选择最高分类特征作为癫痫发作预测相关向量，并制定癫痫发作预测的决策规则。相关向量作为当前脑电图活动的预测器。所提出的决策规则预测正在进行的信号活动是否朝着可能的癫痫发作情况（真或假）发展。所建议的框架显示在实际癫痫发作前10秒预测发作事件。结论：值得一提的是，所提出的研究利用单一线性特征精确预测癫痫发作。此外，使用单一特征有助于降低系统复杂性、处理延迟和系统延迟。
