One can determinate the occurrence of epileptic seizure from the electroencephalogram (EEG) signal. Nonautomatic epilepsy detection is onerous and may be prone to error. They have augmented automated detection of seizure methods to attain accurate results. In view of this research work, we designed a frequency localized optimal filter bank to assess their effectiveness for automatic detection of seizures from EEG records. The basic preferred requirement of optimal filters relies on low bandwidth in the discipline of biomedical signal processing. This work provides a novel filter bank method called optimal equilateral wavelet filter bank (OEWFB) to satisfy the regularity criteria. This regularity constraint is being satisfied with semi‐definite programming (SDP) framework, which specifically does nothing with any parameterization. Implementing the proposed filter banks, it disbands EEG signals into five wavelet sub‐bands. The fuzzy entropy (FuEn), Renyi's entropy (ReEn), and the Kraskov entropy (KrEn) are being used for extracting the features from the wavelet sub‐bands. The P values provide the distinctive ability of the features. Classification with 10‐fold cross‐validation for several classifiers such as quadratic discriminant, linear quadratic discriminant, K‐nearest neighbor, support vector machine, logistic regression, and complex tree is utilized to classify the EEG signals into seizure vs non‐seizure class and seizure‐free vs seizure affected class. The proposed research work has gained the highest accuracy, specificity, sensitivity, and positive predictive values of 99.4%, 99%, 99.66%, and 99.35%, respectively, for class‐1 (ABCD vs E). The performances of the proposed work using the Bonn EEG data set ensure validation concerning compatibility and robustness.

中文翻译：

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一种用于自动检测 EEG 信号癫痫发作的新型双波段等边小波滤波器组方法

可以从脑电图 (EEG) 信号确定癫痫发作的发生。非自动癫痫检测是繁重的并且可能容易出错。他们增强了癫痫发作方法的自动检测，以获得准确的结果。鉴于这项研究工作，我们设计了一个频率局部最优滤波器组来评估它们从 EEG 记录中自动检测癫痫发作的有效性。最佳滤波器的基本首选要求依赖于生物医学信号处理学科中的低带宽。这项工作提供了一种新的滤波器组方法，称为最优等边小波滤波器组 (OEWFB)，以满足规律性标准。半定规划（SDP）框架满足了这种规律性约束，它对任何参数化都不做任何事情。实施所提出的滤波器组，它将 EEG 信号分解为五个小波子带。模糊熵 (FuEn)、仁义熵 (ReEn) 和克拉斯科夫熵 (KrEn) 被用于从小波子带中提取特征。P 值提供了特征的独特能力。对多个分类器（例如二次判别式、线性二次判别式、K 最近邻、支持向量机、逻辑回归和复杂树）进行 10 倍交叉验证的分类用于将 EEG 信号分类为癫痫发作与非癫痫发作类和无癫痫发作 vs 癫痫发作影响等级。对于 1 类（ABCD 与 E），拟议的研究工作获得了最高的准确度、特异性、敏感性和阳性预测值，分别为 99.4%、99%、99.66% 和 99.35%。