Torse Dattaprasad, Desai Veena, Khanai Rajashri
Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka 590008, India.
Department of Electronics and Communication Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, Karnataka 590008, India.
J Biomed Res. 2019 Oct 17;34(3):191-204. doi: 10.7555/JBR.33.20190019.
The detection of seizure onset and events using electroencephalogram (EEG) signals are important tasks in epilepsy research. The literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the cloud. However, seizure monitoring application needs near sensor processing due to privacy and latency issues. In this paper, a real time seizure detection system has been implemented using an embedded system. The proposed system is based on ensemble empirical mode decomposition (EEMD) and tunable-Q wavelet transform (TQWT) algorithms. The analysis and classification of non-stationary EEG signals require the wavelet transform with high -factor. However, direct use of TQWT increases the computational complexity of feature extraction from multivariate EEG signals. In this paper, the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions (IMFs). The Kraskov (KraEn), sample (SampEn), and permutation (PermEn) entropy features of IMFs are extracted and based on optimum values, and 4 IMFs are decomposed using TQWT. Secondly, centered correntropy (CenCorrEn) features of the 1 and 16 sub-band of TQWT have been used as classifier inputs. The performance of multilayer perceptron neural networks (MLPNN), least squares support vector machine (LSSVM), and random forest (RF) classifiers has been tested on the multichannel EEG data recorded from a local hospital. The RF classifier has produced the highest accuracy of 96.2% in classifying the signals. The proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.
利用脑电图（EEG）信号检测癫痫发作起始和事件是癫痫研究中的重要任务。关于癫痫发作检测的现有文献讨论了使用通过云端访问的工具来实现先进的信号处理算法。然而，由于隐私和延迟问题，癫痫监测应用需要在传感器附近进行处理。在本文中，使用嵌入式系统实现了一个实时癫痫检测系统。所提出的系统基于集成经验模态分解（EEMD）和可调Q小波变换（TQWT）算法。对非平稳EEG信号进行分析和分类需要高因子小波变换。然而，直接使用TQWT会增加从多变量EEG信号中提取特征的计算复杂度。在本文中，第一步是使用EEMD处理信号以获得8个本征模态函数（IMF）。提取IMF的Kraskov（KraEn）、样本（SampEn）和排列（PermEn）熵特征，并基于最优值，使用TQWT分解4个IMF。其次，将TQWT的第1层和第16层子带的中心相关熵（CenCorrEn）特征用作分类器输入。在从当地医院记录的多通道EEG数据上测试了多层感知器神经网络（MLPNN）、最小二乘支持向量机（LSSVM）和随机森林（RF）分类器的性能。RF分类器在信号分类中产生了96.2%的最高准确率。所提出的方案已被用于开发嵌入式癫痫检测系统，以协助神经科医生做出癫痫诊断决策。
