新型癫痫发作检测技术及其实证分析
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis
AI总结：
本研究提出一种基于DWT、三种降维算法及三种分类器的癫痫检测框架，在波恩数据集上LDA与NB组合达到100%准确率，优于其他组合。
摘要：
脑电图（EEG）是一种非侵入性检查，用于记录大脑的电活动。该检查有助于诊断各种脑部问题。EEG信号被用于癫痫检测，结合离散小波变换（DWT）和机器学习分类器，可实现癫痫检测。在癫痫发作检测中，主要使用机器学习分类器和统计特征。EEG信号中隐藏的信息有助于检测影响大脑的疾病。有时，在时域和频域中识别EEG的最小变化是复杂的。DWT能够提供信号在不同频带的合适分解以及特征提取。我们使用了三维降维算法，即主成分分析（PCA）、独立成分分析（ICA）和线性判别分析（LDA）。最后，通过融合规则选择特征，并在最后一步，分别使用三种不同的分类器，即支持向量机（SVM）、朴素贝叶斯（NB）和K近邻（KNN）进行分类。所提出的框架在波恩数据集上进行了测试。仿真结果表明，LDA和NB组合的准确率达到100%，优于其他分类器组合，包括LDA和SVM的89.17%、LDA和KNN的80.42%、PCA和NB的89.92%、PCA和SVM的85.58%、PCA和KNN的80.42%、ICA和NB的82.33%、ICA和SVM的90.42%以及ICA和KNN的90%。此外，LDA和NB组合的灵敏度、特异性、准确率、精确率和召回率均为100%。结果证明了该模型的有效性。
英文摘要：
An Electroencephalogram (EEG) is a non-invasive exam that records the brain's electrical activity. This is used to help diagnose conditions such as different brain problems. EEG signals are taken for epilepsy detection, and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, machine learning classifiers and statistical features are mainly used. The hidden information in the EEG signal helps detect diseases affecting the brain. Sometimes it is complicated to identify the minimum changes in the EEG in the time and frequency domain's purpose. The DWT can give a suitable decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step, three different classifiers, Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest-Neighbor (KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset. The simulation results provide 100% accuracy for the LDA and NB combination outperforming accuracy with other classifiers combinations, including 89.17% for LDA and SVM, 80.42% for LDA and KNN, 89.92% for PCA and NB, 85.58% PCA and SVM, 80.42% PCA and KNN, 82.33% for ICA and NB, 90.42% for ICA and SVM, 90% for ICA and KNN. Also, the LDA and NB combination shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. The results prove the effectiveness of this model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
