Behnam Morteza, Pourghassem Hossein
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran.
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran.
Comput Methods Programs Biomed. 2016 Aug;132:115-36. doi: 10.1016/j.cmpb.2016.04.014. Epub 2016 Apr 12.
Epileptic seizure prediction using EEG signal analysis is an important application for drug therapy and pediatric patient monitoring. Time series estimation to obtain the future samples of EEG signal has vital role for detecting seizure attack. In this paper, a novel density-based real-time seizure prediction algorithm based on a trained offline seizure detection algorithm is proposed.
In the offline seizure detection procedure, after signal preprocessing, histogram-based statistical features are extracted from signal probability distribution. By defining a deterministic polynomial model on the normalized histogram, a novel syntactic feature that is named Interpolated Histogram Feature (IHF) is proposed. Moreover, with this feature, Seizure Distribution Model (SDM) as a descriptor of the seizure and non-seizure signals is presented. By using a novel hybrid optimization algorithm based on Bayesian classifier and Hunting Search (HuS) algorithm, the optimal features are selected. To detect the seizure attacks in the online mode, a Multi-Layer Perceptron (MLP) classifier is trained with the optimal features in the offline procedure. For online prediction, the enhanced Recursive Least Square (RLS) filter is applied to estimate sample-by-sample of the EEG signal. Also, a density-based signal tracking scenario is introduced to update and tune the parameters of RLS filtering algorithm.
Our prediction algorithm is evaluated on 104 hours of EEG signals recorded from 23 pediatric patients. Our online signal prediction algorithm provides the accuracy rate of 86.56% and precision rate of 86.53% simultaneously using the trained MLP classifier from the offline mode. The recall rate of seizure prediction is 97.27% and the false prediction rate of 0.00215 per hour is achieved as well. Ultimately, the future samples of EEG signal are estimated, and the time of seizure signal prediction is also converged to 6.64 seconds.
In our proposed real-time algorithm, by implementing a density-based signal tracking scenario, the future samples of signal with suitable time is predicted and the seizure is detected based on the optimal features from the IHF and histogram-based statistical features with acceptable performance.
利用脑电图（EEG）信号分析进行癫痫发作预测是药物治疗和儿科患者监测的一项重要应用。通过时间序列估计来获取EEG信号的未来样本对于检测癫痫发作至关重要。本文提出了一种基于训练好的离线癫痫检测算法的新型基于密度的实时癫痫预测算法。
在离线癫痫检测过程中，信号预处理后，从信号概率分布中提取基于直方图的统计特征。通过在归一化直方图上定义一个确定性多项式模型，提出了一种名为插值直方图特征（IHF）的新型句法特征。此外，利用该特征，提出了癫痫分布模型（SDM）作为癫痫和非癫痫信号的描述符。通过使用一种基于贝叶斯分类器和狩猎搜索（HuS）算法的新型混合优化算法，选择最优特征。为了在在线模式下检测癫痫发作，使用离线过程中的最优特征训练多层感知器（MLP）分类器。对于在线预测，应用增强递归最小二乘（RLS）滤波器逐样本估计EEG信号。此外，引入了一种基于密度的信号跟踪方案来更新和调整RLS滤波算法的参数。
我们的预测算法在从23名儿科患者记录的104小时EEG信号上进行了评估。我们的在线信号预测算法使用离线模式下训练的MLP分类器，同时提供了86.56%的准确率和86.53%的精确率。癫痫发作预测的召回率为97.27%，每小时的误预测率也达到了0.00215。最终，估计了EEG信号的未来样本，癫痫信号预测的时间也收敛到了6.64秒。
在我们提出的实时算法中，通过实施基于密度的信号跟踪方案，预测了具有合适时间的信号未来样本，并基于来自IHF和基于直方图的统计特征的最优特征检测癫痫发作，性能可接受。
