用视觉启发的模型自动识别脑电图中的有害脑活动，准确率媲美专家。
An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework
- 基于视觉预训练框架设计脑电图分类模型，提升泛化能力。
- 在多类脑电活动识别中，准确率最高达0.972，接近人类专家水平。
- 参数量仅为顶尖模型的2.8%，适合临床轻量化部署。
通过脑电图（EEG）及时识别有害脑活动对脑疾病诊断与治疗至关重要，但现有人工智能模型受限于评估者间差异、资源不足及泛化能力差，应用受限。本研究开发并验证了一种卷积神经网络模型VIPEEGNet，基于马萨诸塞总医院/哈佛医学院采集的两组独立数据集（2006–2020年）。训练队列包含1950名患者，共106,800段脑电片段，由至少1名专家标注（最多28名）。在线测试队列来自额外1,532名患者，每段数据由至少10名专家标注。在二分类任务中，VIPEEGNet对癫痫、局灶性慢波放电（LPD）、广泛性慢波放电（GPD）、局灶性快速节律放电（LRDA）、广泛性快速节律放电（GRDA）和“其他”类别的平均AUC分别为0.972、0.962、0.972、0.938、0.949、0.930。多分类任务中，敏感性为36.8%至88.2%，精确率55.6%至80.4%，表现与人类专家相当。外部验证显示，其Kullback-Leibler散度为0.223和0.273，在2,767个竞争算法中排名前二，仅使用第一梯队算法2.8%的参数量。
原文摘要 · Abstract (English)
Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence (AI) models. In this study, a convolutional neural network model, VIPEEGNet, was developed and validated using EEGs recorded from Massachusetts General Hospital/Harvard Medical School. The VIPEEGNet was developed and validated using two independent datasets, collected between 2006 and 2020. The development cohort included EEG recordings from 1950 patients, with 106,800 EEG segments annotated by at least one experts (ranging from 1 to 28). The online testing cohort consisted of EEG segments from a subset of an additional 1,532 patients, each annotated by at least 10 experts. For the development cohort (n=1950), the VIPEEGNet achieved high accuracy, with an AUROC for binary classification of seizure, LPD, GPD, LRDA, GRDA, and "other" categories at 0.972 (95% CI, 0.957-0.988), 0.962 (95% CI, 0.954-0.970), 0.972 (95% CI, 0.960-0.984), 0.938 (95% CI, 0.917-0.959), 0.949 (95% CI, 0.941-0.957), and 0.930 (95% CI, 0.926-0.935). For multi classification, the sensitivity of VIPEEGNET for the six categories ranges from 36.8% to 88.2% and the precision ranges from 55.6% to 80.4%, and performance similar to human experts. Notably, the external validation showed Kullback-Leibler Divergence (KLD)of 0.223 and 0.273, ranking top 2 among the existing 2,767 competing algorithms, while we only used 2.8% of the parameters of the first-ranked algorithm.
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