Hussain Lal
1Quality Enhancement Cell (QEC), The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, Azad Kashmir 13100 Pakistan.
2Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan.
Cogn Neurodyn. 2018 Jun;12(3):271-294. doi: 10.1007/s11571-018-9477-1. Epub 2018 Jan 25.
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.
癫痫是一种由于大脑神经元异常兴奋而产生的神经系统疾病。研究表明，通过对癫痫发作患者的脑电图（EEG）进行监测来检测癫痫发作。基于脑电图检测的癫痫诊断性能需要特征提取策略。在本研究中，我们基于时域和频域特征、非线性、基于小波的熵以及一些统计特征提取了不同的特征提取策略。通过考虑多个因素，使用新型机器学习分类器进行了更深入的研究。基于多类核和盒约束水平对支持向量机核进行了评估。同样，对于K近邻（KNN），我们计算了不同的距离度量、邻居权重和邻居数量。类似地，对于决策树，我们根据最大分裂数和分裂标准调整参数，并基于不同的集成方法和学习率对集成分类器进行评估。采用十折交叉验证进行训练/测试，并以真阳性率（TPR）、假阳性率（NPR）、阳性预测值（PPV）、准确率和曲线下面积（AUC）的形式评估性能。在本研究中，使用多种特征提取策略、强大的机器学习分类器和更先进的优化选项进行了更深入的分析。支持向量机线性核和采用城市街区距离度量的KNN总体准确率最高，达到99.5%，高于使用这些分类器的默认参数时的准确率。此外，使用支持向量机在不同核尺度下获得了最高的分离度（AUC = 0.9991, 0.9990）。此外，采用逆平方距离权重的K近邻在不同邻居数量下表现出更高的性能。此外，为了区分癫痫发作后心率振荡与癫痫发作期受试者，使用不同的机器学习分类器获得了100%的最高性能。
