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Classification of epileptiform events in EEG signals using neural classifier based on SOM

机译：基于SOM的神经分类器对脑电信号中癫痫样事件进行分类

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Analysis of long-term electroencephalogram signals (EEG) is an important tool to clinically confirm the diagnosis of epilepsy. The characteristic electrographic events that represent epilepsy in the analysis of EEG are called epileptiform events (spikes and sharp waves). The process of EEG record analysis is performed by highly trained specialists, which identify the spikes and sharp waves throughout EEG records with minimum duration of 24 hours. Since epileptiform events have typical amplitude around 200μV and duration between 20 and 200ms the analysis of the EEG records is considered very time-consuming and tiring for the experts. Several studies for automatic detection and classification of epileptiform events have been proposed but there is still no system with widespread use and a performance that meets the needs of the specialists. The Self-Organizing Maps of Kohonen (SOM) are an unsupervised neural network algorithm that consists of two layers of neurons that has been successfully used in a wide variety of applications. The objective of this study is to test the feasibility of using Self-Organizing Maps of Kohonen for automatic classification of epileptiform events and non-epileptiform events in EEG signals. Different maps of Kohonen were developed and tested. After simulations, the results were evaluated according to classical performance indexes and the best network achieved 98.7% sensitivity, 91.9% specificity, 90.08% selectivity and 94.8% efficiency. Comparing the results of other SOM studies we obtained sensitivity 9-12% higher and selectivity 12-39% higher than the analyzed studies. Furthermore, a comparison with the results of a previous study that uses the same EEG signal database showed that the overall efficiency was quite similar (only 1% lower).
机译：长期脑电图信号（EEG）的分析是临床上确认癫痫诊断的重要工具。在脑电图分析中代表癫痫的特征性电描记事件称为癫痫样事件（尖峰和尖波）。脑电图记录分析的过程由训练有素的专家执行，他们可以识别出整个脑电图记录中的尖峰和尖波，最短持续时间为24小时。由于癫痫样事件的典型振幅在200μV附近，持续时间在20到200ms之间，因此对EEG记录的分析被认为非常耗时且累人。已经提出了一些对癫痫样事件进行自动检测和分类的研究，但是仍然没有能够广泛使用且性能能够满足专家需求的系统。 Kohonen的自组织映射（SOM）是一种无监督的神经网络算法，由两层神经元组成，已成功应用于各种应用中。这项研究的目的是测试使用Kohonen自组织图谱对EEG信号中的癫痫样事件和非癫痫样事件进行自动分类的可行性。开发并测试了Kohonen的不同地图。经过模拟，根据经典性能指标对结果进行评估，最佳网络实现了98.7％的灵敏度，91.9％的特异性，90.08％的选择性和94.8％的效率。比较其他SOM研究的结果，我们获得的灵敏度比分析的研究高9-12％，选择性高12-39％。此外，与使用相同EEG信号数据库的先前研究结果进行的比较表明，总体效率非常相似（仅降低了1％）。

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