Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran.
Comput Biol Med. 2021 Apr;131:104250. doi: 10.1016/j.compbiomed.2021.104250. Epub 2021 Feb 4.
Epilepsy is a prevalent disorder that affects the central nervous system, causing seizures. In the current study, a novel algorithm is developed using electroencephalographic (EEG) signals for automatic seizure detection from the continuous EEG monitoring data.
In the proposed methods, the discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques are used to extract different coefficients from the EEG signals. Then, some non-linear features, such as fuzzy/approximate/sample/alphabet and correct conditional entropy, along with some statistical features are calculated using the DWT and OMP coefficients. Three widely-used EEG datasets were utilized to assess the performance of the proposed techniques.
The proposed OMP-based technique along with the support vector machine classifier yielded an average specificity of 96.58%, an average accuracy of 97%, and an average sensitivity of 97.08% for different types of classification tasks. Moreover, the proposed DWT-based technique provided an average sensitivity of 99.39%, an average accuracy of 99.63%, and an average specificity of 99.72%.
The experimental findings indicated that the proposed algorithms outperformed other existing techniques. Therefore, these algorithms can be implemented in relevant hardware to help neurologists with seizure detection.
癫痫是一种常见的中枢神经系统疾病，会导致癫痫发作。本研究提出了一种基于脑电信号的新型算法，用于从连续脑电监测数据中自动检测癫痫发作。
在提出的方法中，离散小波变换（DWT）和正交匹配追踪（OMP）技术用于从脑电信号中提取不同的系数。然后，使用 DWT 和 OMP 系数计算一些非线性特征，如模糊/近似/样本/字母和正确条件熵，以及一些统计特征。利用三个广泛使用的脑电数据集评估了所提出技术的性能。
基于 OMP 的技术与支持向量机分类器相结合，对于不同类型的分类任务，平均特异性为 96.58%，平均准确率为 97%，平均灵敏度为 97.08%。此外，基于 DWT 的技术提供了平均灵敏度为 99.39%，平均准确率为 99.63%，平均特异性为 99.72%。
实验结果表明，所提出的算法优于其他现有技术。因此，这些算法可以在相关硬件中实现，以帮助神经科医生进行癫痫发作检测。
