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首页> 美国卫生研究院文献>Cognitive Neurodynamics >Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach
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Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach

机译：通过使用先进的参数优化方法使用强大的机器学习分类技术以不同的特征提取策略检测癫痫发作

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Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.
机译：癫痫病是由于大脑中神经元的异常兴奋性而产生的神经系统疾病。该研究表明，通过癫痫发作的患者的脑电图（EEG）监测大脑活动，以检测癫痫发作。基于癫痫的脑电图检测的性能需要特征提取策略。在这项研究中，我们提取了基于时域和频域特征，非线性，基于小波的熵和少量统计特征的各种特征提取策略。通过考虑多种因素，使用新颖的机器学习分类器进行了更深入的研究。支持向量机内核是根据多类内核和盒约束级别进行评估的。同样，对于K近邻（KNN），我们计算了不同的距离度量，邻居权重和邻居。同样，我们根据最大拆分和拆分标准对参数进行了调整的决策树，并根据不同的集成方法和学习率对集成分类器进行了评估。对于培训/测试，使用十倍交叉验证，并以TPR，NPR，PPV，准确性和AUC形式评估性能。在这项研究中，通过使用功能强大的机器学习分类器和更高级的最佳选择的各种特征提取策略，进行了更深入的分析方法。支持向量机线性核和具有City Block距离度量的KNN给出了99.5％的总体最高准确度，比使用这些分类器的默认参数要高。此外，使用SVM在不同的内核规模下获得了最高的分离度（AUC = 0.9991，0.9990）。此外，距离平方成反比的K最近邻在不同邻居处具有更高的性能。此外，为了区分癫痫发作的受试者的心跳率，使用不同的机器学习分类器可获得100％的最高性能。

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