摘 要
癫痫是一种常见的神经系统疾病，其诊断和监测依赖于对脑电活动的精确分析。本研究旨在探讨脑电图（EEG）数据分析在癫痫检测中的应用潜力，并提出一种基于机器学习的新型算法以提高检测精度。研究首先收集了包含癫痫发作期与非发作期的多组EEG数据，通过信号预处理技术去除噪声干扰并提取特征参数，包括频域、时域及非线性动力学指标。随后，采用深度学习模型结合传统分类器构建混合预测框架，该框架能够有效区分癫痫发作与正常脑电模式。实验结果表明，所提出的算法在敏感性、特异性和准确性方面均优于现有方法，尤其是在复杂背景信号下的检测性能更为稳定。此外，研究还发现特定频段的能量变化与癫痫发作存在显著相关性，这一发现为临床早期预警提供了理论支持。本研究的主要创新点在于整合多源特征信息并优化分类策略，从而提升了癫痫检测的智能化水平，为未来个性化医疗方案的设计奠定了基础。研究成果不仅有助于改善癫痫患者的诊疗体验，也为脑电数据分析领域的进一步发展开辟了新方向。
关键词：癫痫检测；脑电图分析；机器学习；特征提取；深度学习模型
Application of EEG Data Analysis in Epilepsy Detection
Abstract: Epilepsy is a common neurological disorder, and its diagnosis and monitoring rely on precise analysis of brain electrical activity. This study investigates the potential application of electroencephalogram (EEG) data analysis in epilepsy detection and proposes a novel machine-learning-based algorithm to enhance detection accuracy. Initially, multiple sets of EEG data, including both ictal and interictal periods, were collected. Signal preprocessing techniques were applied to remove noise interference and extract feature parameters, encompassing frequency-domain, time-domain, and nonlinear dynamics indices. Subsequently, a hybrid prediction fr amework combining deep learning models with traditional classifiers was developed, which effectively distinguishes epileptic seizures from normal EEG patterns. Experimental results demonstrate that the proposed algorithm outperforms existing methods in terms of sensitivity, specificity, and overall accuracy, particularly exhibiting greater stability in detecting seizures under complex background signals. Additionally, the study reveals a significant correlation between energy changes in specific frequency bands and seizure occurrences, providing theoretical support for clinical early warning systems. The primary innovation of this research lies in the integration of multi-source feature information and optimization of classification strategies, thereby advancing the intelligence level of epilepsy detection and laying a foundation for the design of future personalized medical solutions. The findings not only contribute to improving the diagnostic and therapeutic experience for epilepsy patients but also open new avenues for the further development of EEG data analysis.
Keywords: Epilepsy Detection; Electroencephalogram Analysis; Machine Learning; Feature Extraction; Deep Learning Model
目 录
1绪论 1
1.1脑电图数据分析的背景与意义 1
1.2癫痫检测领域的研究现状综述 1
1.3本文研究方法与技术路线 2
2脑电图数据预处理技术 2
2.1数据采集与质量评估方法 2
2.2噪声去除与信号增强技术 3
2.3特征提取与降维算法分析 3
2.4预处理对癫痫检测的影响评估 4
2.5数据标准化与一致性保障 4
3癫痫检测算法设计与优化 5
3.1常见癫痫检测算法概述 5
3.2机器学习在癫痫检测中的应用 6
3.3深度学习模型的设计与改进 6
3.4算法性能评价指标体系构建 7
3.5实验结果对比与优化策略 7
4脑电图数据分析的实际应用 8
4.1医疗场景中的脑电图数据应用 8
4.2不同年龄段患者的检测效果分析 8
4.3实时监测系统的开发与实现 9
4.4数据隐私保护与伦理问题探讨 9
4.5应用前景与未来发展方向 10
结论 10
参考文献 12
致 谢 13
