论文标题
使用小波变换进行癫痫发作预测的原理组件分析
Principle components analysis for seizures prediction using wavelet transform
论文作者
论文摘要
癫痫是一种疾病，其中由于神经元的异常活性而频繁癫痫发作。受这种疾病影响的患者可以在药物或手术程序的帮助下进行治疗。但是，这两种方法都不是很有用。有效治疗癫痫患者的唯一方法是预测癫痫发作之前的癫痫发作。已经观察到，在癫痫发作的发生之前，脑信号的异常活性就开始了。许多研究人员提出了机器学习模型，以通过检测前状态的开始来预测癫痫发作。但是，在预测前状态的预测中，预处理，特征提取和分类仍然是一个巨大的挑战。因此，我们提出了一个模型，该模型使用常见的空间模式滤波和小波变换进行预处理，用于特征提取的主成分分析和支持向量机以检测前态状态。我们已经将模型应用于23名受试者，并且已经观察到84次癫痫发作的平均灵敏度为93.1％。
Epilepsy is a disease in which frequent seizures occur due to abnormal activity of neurons. Patients affected by this disease can be treated with the help of medicines or surgical procedures. However, both of these methods are not quite useful. The only method to treat epilepsy patients effectively is to predict the seizure before its onset. It has been observed that abnormal activity in the brain signals starts before the occurrence of seizure known as the preictal state. Many researchers have proposed machine learning models for prediction of epileptic seizures by detecting the start of preictal state. However, pre-processing, feature extraction and classification remains a great challenge in the prediction of preictal state. Therefore, we propose a model that uses common spatial pattern filtering and wavelet transform for preprocessing, principal component analysis for feature extraction and support vector machines for detecting preictal state. We have applied our model on 23 subjects and an average sensitivity of 93.1% has been observed for 84 seizures.
