用生成模型合成罕见病脑电数据，提升肌萎缩侧索硬化诊断准确率
Synthetic ALS-EEG Data Augmentation for ALS Diagnosis Using Conditional WGAN with Weight Clipping
- 基于条件生成对抗网络，学习并生成逼真肌萎缩侧索硬化脑电信号
- 合成数据使分类器在不平衡数据下准确率显著提升
- 适合神经科学与医疗AI研究者，助力罕见病数据匮乏问题
肌萎缩侧索硬化（ALS）是一种罕见的神经退行性疾病，患者高质量脑电图（EEG）数据稀缺。数据不足与健康对照组和ALS组之间严重的类别不平衡，给机器学习分类器的训练带来挑战。本文通过条件水印生成对抗网络（CWGAN）生成ALS患者的合成脑电信号来解决此问题。我们在一个私有脑电数据集（ALS vs. 非ALS）上训练CWGAN，以学习ALS脑电分布并生成真实感强的合成样本。对脑电记录进行预处理与归一化后，构建了用于生成合成ALS信号的CWGAN模型。详细描述了模型架构与训练流程，关键超参数选择以确保训练稳定。定性评估显示，生成信号与真实ALS脑电模式高度相似；生成器与判别器损失曲线收敛稳定，表明模型成功学习。合成脑电信号外观真实，具有作为增强数据用于训练分类器的潜力，有助于缓解类别不平衡问题，提升ALS检测精度。本文还讨论了该方法在促进数据共享与提升诊断模型方面的应用前景。
原文摘要 · Abstract (English)
Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease, and high-quality EEG data from ALS patients are scarce. This data scarcity, coupled with severe class imbalance between ALS and healthy control recordings, poses a challenge for training reliable machine learning classifiers. In this work, we address these issues by generating synthetic EEG signals for ALS patients using a Conditional Wasserstein Generative Adversarial Network (CWGAN). We train CWGAN on a private EEG dataset (ALS vs. non-ALS) to learn the distribution of ALS EEG signals and produce realistic synthetic samples. We preprocess and normalize EEG recordings, and train a CWGAN model to generate synthetic ALS signals. The CWGAN architecture and training routine are detailed, with key hyperparameters chosen for stable training. Qualitative evaluation of generated signals shows that they closely mimic real ALS EEG patterns. The CWGAN training converged with generator and discriminator loss curves stabilizing, indicating successful learning. The synthetic EEG signals appear realistic and have potential use as augmented data for training classifiers, helping to mitigate class imbalance and improve ALS detection accuracy. We discuss how this approach can facilitate data sharing and enhance diagnostic models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。