Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India.
School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Comput Methods Programs Biomed. 2023 Oct;240:107680. doi: 10.1016/j.cmpb.2023.107680. Epub 2023 Jun 22.
Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in temporary changes in behavior, level of consciousness, and involuntary movements. An accurate prediction of seizures can improve the standard of living in epileptic subjects. The increasing capabilities of machine learning and computer-assisted devices can detect seizures accurately with minimal human intervention.
This paper proposes a method to detect seizure and non-seizure events using superlet transform (SLT) and a deep convolution neural network: VGG-19. The electroencephalogram (EEG) dataset from the University of Bonn is used to validate the efficacy of the proposed method.
SLT, a high-resolution time-frequency technique, converts EEG records into two-dimensional (2-D) images. SLT provides a high-resolution time-frequency representation reflecting the oscillation bursts in an EEG record. The time-frequency representations as 2-D images are fed to a pre-trained convolutional neural network: VGG-19. The last layers of VGG-19 are replaced with new layers to accommodate the different classification problems.
The proposed method achieved an accuracy of 100% for all seven seizure and non-seizure detection cases considered in this work. In the case of three and five-class classification problems, the proposed method has better accuracy than other existing methods. The CHB-MIT scalp EEG database is also used to assess the effectiveness of the proposed method, which achieved a classification accuracy of 94.3% in distinguishing between seizure and non-seizure events.
The results obtained using the proposed methodology show the efficacy of the proposed method in accurately detecting seizures and other brain activity with the least pre-processing and human involvement. The proposed method can assist medical practitioners by saving their effort and time.
癫痫是一种慢性脑部疾病，其特征是反复发作，大约影响 5000 万人。反复发作是其特征。癫痫发作是脑细胞之间不受控制的电活动爆发，导致行为、意识水平和不自主运动的暂时变化。准确预测癫痫发作可以提高癫痫患者的生活质量。机器学习和计算机辅助设备的能力不断提高，可以在最小的人为干预下准确检测癫痫发作。
本文提出了一种使用超小波变换（SLT）和深度卷积神经网络：VGG-19 检测癫痫发作和非癫痫发作事件的方法。使用波恩大学的脑电图（EEG）数据集验证所提出方法的功效。
SLT 是一种高分辨率时频技术，它将 EEG 记录转换为二维（2-D）图像。SLT 提供了一种高分辨率时频表示，反映了 EEG 记录中的振荡爆发。时频表示作为 2-D 图像被馈送到预训练的卷积神经网络：VGG-19。VGG-19 的最后几层被替换为新的层，以适应不同的分类问题。
所提出的方法在本工作中考虑的所有七个癫痫发作和非癫痫发作检测案例中均达到了 100%的准确率。在三分类和五分类问题的情况下，所提出的方法比其他现有方法具有更高的准确率。还使用 CHB-MIT 头皮 EEG 数据库评估了所提出方法的有效性，该方法在区分癫痫发作和非癫痫发作事件方面的分类准确率达到了 94.3%。
所提出方法的结果表明，所提出的方法在最小的预处理和人为干预下准确检测癫痫发作和其他脑活动的有效性。所提出的方法可以通过节省医疗工作者的精力和时间来帮助他们。
