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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >A NEURAL NETWORK-BASED CLASSIFICATION MODEL FOR PARTIAL EPILEPSY BY EEG SIGNALS
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A NEURAL NETWORK-BASED CLASSIFICATION MODEL FOR PARTIAL EPILEPSY BY EEG SIGNALS

机译：基于神经网络的脑电信号部分性癫痫分类模型

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Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify subgroups of partial epilepsy by Multilayer Perceptron Neural Networks (MLPNNs). This is the first study to classify the partial epilepsy groups using the neural network according to EEG signals. 418 patients with epilepsy diagnoses according to International League against Epilepsy (ILAE, 1981) were included in this study. The epilepsy outpatients at the Neurology Department Clinic of Cukurova University Medical School between the years of 2002-2005 were examined and included in the study. The MLPNNs were trained by the parameters obtained from the EEG signals and clinical findings of the patients. Test results show that the MLPNN model is able to classify partial epilepsy with an accuracy of 91.5%. Moreover, new MLPNNs were constructed for determining significant variables on classification. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. In conclusion, we think that the classification performance of MLPNN model for partial epilepsy is satisfactory and this model may be used in clinical studies as a decision support tool to determine the partial epilepsy classification of the patients.
机译：癫痫是皮质兴奋性疾病，并且仍然是重要的医学问题。对患者的癫痫综合症的正确诊断可以澄清药物治疗的选择，并且还可以在许多情况下准确评估预后。这项研究的目的是评估癫痫患者，并通过多层感知器神经网络（MLPNNs）对部分癫痫的亚组进行分类。这是第一个使用神经网络根据脑电信号对部分性癫痫患者进行分类的研究。根据国际抗癫痫联盟（ILAE，1981年）对418例癫痫病患者进行了诊断。研究人员对2002-2005年间库库洛娃大学医学院神经内科门诊的癫痫患者进行了检查，并将其纳入研究。通过从脑电信号和患者的临床发现中获得的参数来训练MLPNN。测试结果表明，MLPNN模型能够以91.5％的准确度对部分癫痫进行分类。此外，构建了新的MLPNN，用于确定分类中的重要变量。在癫痫发作时间变量过程中失去意识会导致分类准确性的下降幅度最大。总之，我们认为MLPNN模型对部分性癫痫的分类性能令人满意，该模型可在临床研究中用作确定患者部分性癫痫分类的决策支持工具。

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