arXiv 2501.163342024-12-24eess.SPcs.LG

基于RNN的癫痫患者癫痫发作起始预测模型

RNN-Based Models for Predicting Seizure Onset in Epileptic Patients

SRM Institute of Science and Technology（SRM科学技术学院）；Emporia State University（恩波利亚州立大学）；Mohan Babu University（莫汉巴布大学）

Mathan Kumar Mounagurusamy, Thiyagarajan V S, Abdur Rahman, Shravan Chandak, D. Balaji, Venkateswara Rao Jallepalli

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出一种基于LSTM的RNN模型，从原始EEG数据提取时间特征以动态适应患者模式，实现了90.2%灵敏度和93的AUC-ROC，显著降低误报率并提升计算效率，有效改善癫痫发作预测的可靠性。

摘要：

癫痫患者的早期管理和更好的临床结果依赖于癫痫发作预测。现有系统的准确性和误报率通常因其对静态阈值和基本脑电图（EEG）特性的依赖而受损。为了克服这些限制，文章提出了一种新颖的基于循环神经网络（RNN）的癫痫发作起始预测方法。与传统技术不同，该系统利用长短期记忆（LSTM）网络从未经处理的EEG数据中提取时间相关性。这使系统能够动态适应每位患者独特的EEG模式，从而提高预测准确性。该系统的方法论包括全面的数据收集、预处理和基于LSTM的特征提取。随后使用带注释的EEG数据集进行模型训练和验证。结果显示，误报率大幅降低（平均为6.8%），预测准确性得到提高（90.2%的灵敏度，88.9%的特异性，以及93的AUC-ROC）。此外，计算效率显著高于现有系统（12毫秒处理时间，45 MB内存消耗）。关于提高癫痫发作预测的可靠性，这些结果证明了所提出的基于RNN策略的有效性，为其在改善癫痫治疗方面的实际应用开辟了可能性。

英文摘要：

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method for seizure start prediction is proposed in the article to overcome these limitations. As opposed to conventional techniques, the proposed system makes use of Long Short-Term Memory (LSTM) networks to extract temporal correlations from unprocessed EEG data. It enables the system to adapt dynamically to the unique EEG patterns of each patient, improving prediction accuracy. The methodology of the system comprises thorough data collecting, preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are then used for model training and validation. Results show a considerable reduction in false alarm rates (average of 6.8%) and an improvement in prediction accuracy (90.2% sensitivity, 88.9% specificity, and AUC-ROC of 93). Additionally, computational efficiency is significantly higher than that of existing systems (12 ms processing time, 45 MB memory consumption). About improving seizure prediction reliability, these results demonstrate the effectiveness of the proposed RNN-based strategy, opening up possibilities for its practical application to improve epilepsy treatment.

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。