In this paper, we detect the occurrence of epileptic seizures in patients as well as activities namely stand, walk, and exercise in healthy persons, leveraging EEG (electroencephalogram) signals. Using Hilbert vibration decomposition (HVD) on non-linear and non-stationary EEG signal, we obtain multiple monocomponents varying in terms of amplitude and frequency. After decomposition, we extract features from the monocomponent matrix of the EEG signals. The instantaneous amplitude of the HVD monocomponents varies because of the motion artifacts present in EEG signals. Hence, the acquired statistical features from the instantaneous amplitude help in identifying the epileptic seizures and the normal human activities. The features selected by correlation-based Q-score are classified using an LSTM (Long Short Term Memory) based deep learning model in which the feature-based weight update maximizes the classification accuracy. For epilepsy diagnosis using the Bonn dataset and activity recognition leveraging our Sensor Networks Research Lab (SNRL) data, we achieve testing classification accuracies of 96.00% and 83.30% respectively through our proposed method.

在本文中，我们利用 EEG（脑电图）信号检测患者癫痫发作的发生以及健康人的站立、行走和运动等活动。对非线性和非平稳 EEG 信号使用希尔伯特振动分解 (HVD)，我们获得了在幅度和频率方面变化的多个单分量。分解后，我们从 EEG 信号的单分量矩阵中提取特征。由于 EEG 信号中存在运动伪影，HVD 单分量的瞬时幅度会发生变化。因此，从瞬时振幅中获得的统计特征有助于识别癫痫发作和正常的人类活动。通过基于相关性的 Q-score 选择的特征使用基于 LSTM（长短期记忆）的深度学习模型进行分类，其中基于特征的权重更新最大限度地提高了分类精度。对于使用波恩数据集的癫痫诊断和利用我们的传感器网络研究实验室 (SNRL) 数据的活动识别，我们通过我们提出的方法分别实现了 96.00% 和 83.30% 的测试分类准确率。