Department of Information Technologies and Communications, University of La Coruña, Campus Elviña, 15071 A Coruña, Spain.
J Neurosci Methods. 2010 Aug 15;191(1):101-9. doi: 10.1016/j.jneumeth.2010.05.020. Epub 2010 Jun 2.
About 1% of the people in the world suffer from epilepsy. The main characteristic of epilepsy is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings can provide valuable information for understanding the mechanisms behind epileptic disorders. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Wavelet transform (WT) is an effective analysis tool for non-stationary signals, such as EEGs. The line length feature reflects the waveform dimensionality changes and is a measure sensitive to variation of the signal amplitude and frequency. This paper presents a novel method for automatic epileptic seizure detection, which uses line length features based on wavelet transform multiresolution decomposition and combines with an artificial neural network (ANN) to classify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A famous public dataset was used to evaluate the proposed method. The high accuracy obtained for three different classification problems testified the great success of the method.
全世界大约有 1%的人患有癫痫。癫痫的主要特征是反复发作。仔细分析脑电图（EEG）记录可以为理解癫痫障碍背后的机制提供有价值的信息。由于癫痫发作不规则且不可预测，因此非常需要在 EEG 记录中自动检测癫痫发作。小波变换（WT）是一种用于非平稳信号（如 EEG）的有效分析工具。线长特征反映了波形的维度变化，是一种对信号幅度和频率变化敏感的度量。本文提出了一种基于小波变换多分辨率分解的线长特征的自动癫痫发作检测新方法，并结合人工神经网络（ANN）对存在或不存在癫痫发作的 EEG 信号进行分类。据作者所知，文献中尚无类似的工作。使用一个著名的公共数据集来评估所提出的方法。对于三个不同的分类问题，所获得的高精度证明了该方法的巨大成功。
