癫痫发作预测的比较分析：探索多样化的预处理技术与机器学习模型
Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models
AI总结：
本文比较了五种机器学习模型在EEG癫痫发作预测中的表现，发现极端随机树（ET）模型以99.29%的准确率优于其他模型及已有最优结果。
摘要：
癫痫是一种常见的神经系统疾病，其特征是反复且不可预测的发作，因此需要准确的预测以实现有效管理和患者护理。将机器学习（ML）应用于脑电图（EEG）记录，并利用其在发作期间提供有价值的大脑活动洞察的能力，能够使准确且稳健的发作预测成为相关研究中不可或缺的组成部分。在本研究中，我们对五种机器学习模型——随机森林（RF）、决策树（DT）、极端随机树（ET）、逻辑回归（LR）和梯度提升（GB）——使用EEG数据进行癫痫发作预测，进行了全面的比较分析。数据集经过了细致的预处理，包括清洗、归一化、异常值处理和过采样，确保了数据质量并促进了准确的模型训练。这些预处理技术在提升模型性能方面发挥了关键作用。我们的分析结果展示了每个模型在准确率方面的表现。LR分类器达到了56.95%的准确率，而GB和DT均达到了97.17%的准确率。RT达到了更高的98.99%准确率，而ET模型表现最佳，准确率为99.29%。我们的研究结果表明，ET模型不仅在比较分析中优于其他模型，还超越了先前研究中的最先进结果。ET模型的卓越性能使其成为使用EEG数据进行准确且稳健的癫痫发作预测的有力选择。
英文摘要：
Epilepsy is a prevalent neurological disorder characterized by recurrent and unpredictable seizures, necessitating accurate prediction for effective management and patient care. Application of machine learning (ML) on electroencephalogram (EEG) recordings, along with its ability to provide valuable insights into brain activity during seizures, is able to make accurate and robust seizure prediction an indispensable component in relevant studies. In this research, we present a comprehensive comparative analysis of five machine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the prediction of epileptic seizures using EEG data. The dataset underwent meticulous preprocessing, including cleaning, normalization, outlier handling, and oversampling, ensuring data quality and facilitating accurate model training. These preprocessing techniques played a crucial role in enhancing the models' performance. The results of our analysis demonstrate the performance of each model in terms of accuracy. The LR classifier achieved an accuracy of 56.95%, while GB and DT both attained 97.17% accuracy. RT achieved a higher accuracy of 98.99%, while the ET model exhibited the best performance with an accuracy of 99.29%. Our findings reveal that the ET model outperformed not only the other models in the comparative analysis but also surpassed the state-of-the-art results from previous research. The superior performance of the ET model makes it a compelling choice for accurate and robust epileptic seizure prediction using EEG data.
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。
