Digitalization and automation are the two aspects in the medical industry that define compliance with industry 4.0. Automation is essential for speeding up the diagnosis process, while digitalization leads to smart medicine and efficient diagnosis. Epilepsy is one such disease that can use these automation techniques. The automatic monitoring of epilepsy EEG is of great significance in clinical medicine. Aiming at the non-stationary characteristics of EEG signals, the classification of EEG signals is based on the combination of overall empirical mode. It is proposed using the random forest method. The EEG signal data set has an epileptic interval over 200 single-channel signals with a seizure period. A total of 819,400 data are used as samples. First, the overall epileptic EEG signal modal is decomposed into multiple intrinsic modal functions. The effective features are extracted from the first-order intrinsic modal function. Finally, random forest and Least Square SVM (LS-SVM) are considered to classify the EEG signals characteristics. The correct recognition rate of random forest and LS-SVM is compared. The results show that random forest classification method has an ideal classification effect on epilepsy EEG signals during and between seizures. The recognition accuracy is 99% and 60%, which is higher than the accuracy of the LS-SVM. The proposed method improves clinical epilepsy. The efficiency of EEG signals analysis.

中文翻译：

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使用新型改良随机森林方法的 EEG 信号分类自动化

数字化和自动化是医疗行业定义符合工业 4.0 的两个方面。自动化对于加快诊断过程至关重要，而数字化则带来智能医疗和高效诊断。癫痫是一种可以使用这些自动化技术的疾病。癫痫脑电图的自动监测在临床医学中具有重要意义。针对脑电信号的非平稳特性，脑电信号的分类是基于整体经验模型的组合。建议使用随机森林方法。脑电信号数据集有超过200个单通道信号的癫痫发作周期。总共使用了 819,400 条数据作为样本。首先，将整体癫痫脑电信号模态分解为多个内在模态函数。从一阶固有模态函数中提取有效特征。最后，考虑使用随机森林和最小二乘支持向量机 (LS-SVM) 对 EEG 信号特征进行分类。比较了随机森林和LS-SVM的正确识别率。结果表明，随机森林分类方法对癫痫发作期间和发作间期的脑电信号具有理想的分类效果。识别准确率分别为99%和60%，高于LS-SVM的准确率。所提出的方法改善了临床癫痫。脑电信号分析的效率。结果表明，随机森林分类方法对癫痫发作期间和发作间期的脑电信号具有理想的分类效果。识别准确率分别为99%和60%，高于LS-SVM的准确率。所提出的方法改善了临床癫痫。脑电信号分析的效率。结果表明，随机森林分类方法对癫痫发作期间和发作间期的脑电信号具有理想的分类效果。识别准确率分别为99%和60%，高于LS-SVM的准确率。所提出的方法改善了临床癫痫。脑电信号分析的效率。