AI系统自动分析脑电图背景活动并生成报告，提升基层医疗诊断准确率。
A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation
- 融合深度学习与专家算法，自动识别脑电图节律和异常
- 对普遍性背景慢化检测优于神经科医生（F1: 0.93 vs 0.82）
- 可用大语言模型生成报告，准确率达100%，适合资源有限医院
脑电图（EEG）在神经疾病诊断中至关重要，但小医院常缺乏先进分析系统，人工读图易出错。本文提出一种混合人工智能系统，实现脑电图背景活动的自动解读与报告生成。系统结合深度学习模型预测后部优势节律（PDR），采用无监督方法去除伪影，并运用专家设计算法检测异常。基于1530份标注脑电图训练，最佳集成模型在PDR预测中达到均方误差（RMSE）0.359，平均绝对误差（MAE）0.237，0.6Hz误差内准确率91.8%，1.2Hz误差内准确率99%。该系统在普遍性背景慢化检测上显著优于神经科医生（p=0.02；F1: AI 0.93，医生0.82），对局灶性异常检测也表现更优（F1: AI 0.71，医生0.55，p=0.79）。在内部数据集与坦普尔大学异常脑电图语料库上验证，F1值分别为0.884和0.835（p=0.66），表现稳定。利用大语言模型生成报告，经三组独立大模型验证，准确率达100%。该系统为资源受限环境提供可扩展、高精度的脑电图分析方案，有助于提升诊断准确率、减少误诊。
原文摘要 · Abstract (English)
Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) system for automatic interpretation of EEG background activity and report generation. The system combines deep learning models for posterior dominant rhythm (PDR) prediction, unsupervised artifact removal, and expert-designed algorithms for abnormality detection. For PDR prediction, 1530 labeled EEGs were used, and the best ensemble model achieved a mean absolute error (MAE) of 0.237, a root mean square error (RMSE) of 0.359, an accuracy of 91.8% within a 0.6Hz error, and an accuracy of 99% within a 1.2Hz error. The AI system significantly outperformed neurologists in detecting generalized background slowing (p = 0.02; F1: AI 0.93, neurologists 0.82) and demonstrated improved focal abnormality detection, although not statistically significant (p = 0.79; F1: AI 0.71, neurologists 0.55). Validation on both an internal dataset and the Temple University Abnormal EEG Corpus showed consistent performance (F1: 0.884 and 0.835, respectively; p = 0.66), demonstrating generalizability. The use of large language models (LLMs) for report generation demonstrated 100% accuracy, verified by three other independent LLMs. This hybrid AI system provides an easily scalable and accurate solution for EEG interpretation in resource-limited settings, assisting neurologists in improving diagnostic accuracy and reducing misdiagnosis rates.
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