Detection of epilepsy disease from EEG signals with artificial neural networks

Cansu Özkan, Seda Doğan, T. Uğur, M. Aksahin, A. Erdamar
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引用次数: 1

Abstract

The diagnosis of the epilepsy diseases are made by physicians with analyzing the electroencephalography (EEG) records. The epilepsy diseases can be determined with observing the main properties of before and on-time seizure signals in time and frequency domain. Physicians are evaluating the results after some necessary scoring on EEG records. However, this evaluation is specialistic, time consuming processes and also may subjective results. At this point, to allow detection of epilepsy diseases, a decision support system can give more objective results to the physicians for diagnosing. The subject of the study is automatically diagnosing the epilepsy diseases on EEG signals. In the proposed study, analyses of EEG signals in time and frequency domain were done and features of diseases were obtained. As a result, using artificial neural network (ANN) and obtained features, a decision support system is realized to diagnose the epilepsy. The specificity and the sensitivity of the algorithm are 94% and 66% respectively.
用人工神经网络从脑电图信号中检测癫痫
癫痫病的诊断是由医生通过分析脑电图(EEG)记录来完成的。通过观察癫痫发作前和发作时信号在时间和频率上的主要特征,可以判断癫痫的疾病。医生在对脑电图记录进行必要的评分后评估结果。然而,这种评估是一个专业的、耗时的过程,也可能产生主观的结果。此时,为了检测癫痫疾病,决策支持系统可以为医生提供更客观的诊断结果。研究对象是利用脑电图信号自动诊断癫痫疾病。在本研究中,对脑电信号进行时域和频域分析,得到疾病的特征。利用人工神经网络(ANN)和获得的特征,实现了癫痫诊断的决策支持系统。该算法的特异性为94%,灵敏度为66%。
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