Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan.
Engineering and Design Department, Western Washington University, Bellingham, WA 98225, USA.
Sensors (Basel). 2023 Apr 19;23(8):4112. doi: 10.3390/s23084112.
This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.
本文提出了一种可训练的混合方法，该方法涉及浅层自动编码器（AE）和传统分类器，用于癫痫发作检测。通过将通道的脑电图（EEG）信号段（EEG 时段）的编码 AE 表示作为特征向量进行分类，将其分类为癫痫和非癫痫。基于单个通道的分析和算法的低计算复杂度允许在使用一个或几个 EEG 通道的身体传感器网络和可穿戴设备中使用，以实现佩戴舒适性。这使得可以在家中对癫痫患者进行扩展诊断和监测。基于训练浅层 AE 以最小化信号重建误差，获得 EEG 信号段的编码表示。通过对分类器进行广泛的实验，我们提出了我们的混合方法的两个版本：（a）与使用 k-最近邻（kNN）分类器的报告方法相比，分类性能最佳的版本，（b）具有硬件友好的体系结构，并且与该类别中使用支持向量机（SVM）分类器的其他报告方法相比，分类性能最佳。该算法在波士顿儿童医院、马萨诸塞理工学院（CHB-MIT）和波恩大学 EEG 数据集上进行了评估。使用 kNN 分类器，该方法在 CHB-MIT 数据集上的准确率、灵敏度和特异性分别达到 98.85%、99.29%和 98.86%。使用 SVM 分类器的最佳精度、灵敏度和特异性分别为 99.19%、96.10%和 99.19%。我们的实验证明了使用浅层结构的 AE 方法生成低维但有效的 EEG 信号表示的优越性，该方法能够以单通道 EEG 水平和 1 秒 EEG 时段的精细粒度进行高性能异常癫痫发作活动检测。
