Padmapriya P, Rajamani V
Department of Biomedical Engineering, SRM Institute of Science and Technology (Deemed to Be University), Ramapuram Campus, Chennai, Tamil Nadu, India.
Department of Electronics and Communication Engineering, Chettinad Academy of Research and Education, Manamai Campus, Chennai, Tamil Nadu, India.
Behav Neurol. 2025 Aug 26;2025:5916201. doi: 10.1155/bn/5916201. eCollection 2025.
Temporary disturbances in brain function are caused by epilepsy, a chronic disorder resulting from sudden abnormal firing of brain neurons. This research introduces an innovative real-time methodology representing detecting epileptic spasms from electroencephalogram (EEG) data. It employs a support vector machine (SVM) alongside embedded zero tree wavelet (EZW) transform. To facilitate precise multiresolution analysis of epileptic convulsions, the EZW method is selected for its capacity to efficiently compress multichannel EEG data while preserving crucial diagnostic features. EZW effectively captures and encodes key patterns in EEG signals, enabling detailed analysis of the subtle variations associated with seizures. This study extracts statistical features such as entropy, kurtosis, skewness, and mean from the compressed EEG segments. These features are then classified using the SVM to distinguish between normal and epileptic states. With a remarkable 99.02% classification accuracy and a false positive rate of only 1.1%, the proposed algorithm demonstrates excellent performance. The novelty lies in integrating SVM with EZW-based feature extraction and advanced preprocessing, enabling efficient real-time EEG analysis. Unlike previous works, this approach preserves critical information, enhances classification accuracy, and supports multichannel signals, offering a robust and practical solution for real-time epilepsy detection. Based on these findings, the method is considered highly suitable for real-time implementation in clinical environments.
癫痫是一种由大脑神经元突然异常放电引起的慢性疾病，会导致大脑功能出现暂时紊乱。本研究引入了一种创新的实时方法，用于从脑电图（EEG）数据中检测癫痫痉挛。该方法采用支持向量机（SVM）并结合嵌入式零树小波（EZW）变换。为便于对癫痫惊厥进行精确的多分辨率分析，选择EZW方法是因为它能够在保留关键诊断特征的同时，有效压缩多通道EEG数据。EZW能有效捕捉并编码EEG信号中的关键模式，从而能够对与癫痫发作相关的细微变化进行详细分析。本研究从压缩后的EEG片段中提取熵、峰度、偏度和均值等统计特征。然后使用SVM对这些特征进行分类，以区分正常状态和癫痫状态。所提出的算法具有99.02%的显著分类准确率和仅1.1%的误报率，表现出色。其新颖之处在于将SVM与基于EZW的特征提取及先进的预处理相结合，实现了高效的实时EEG分析。与以往的工作不同，这种方法保留了关键信息，提高了分类准确率，并支持多通道信号，为实时癫痫检测提供了一种强大而实用的解决方案。基于这些发现，该方法被认为非常适合在临床环境中进行实时应用。
