通过合成数据预训练提升心电图分类性能
Boosting ECG Classification Performance by Pre-training with Synthesized Data
AI总结：
提出基于医学知识的高斯组合合成算法生成单导联II心电图数据，用于预训练深度神经网络，在四种异常分类中平均提升最高33.2%，尤其在小数据集场景下效果显著。
摘要：
深度神经网络通常需要大量数据集才能有效训练。在医学领域，由于隐私问题和某些疾病的罕见性，获取大规模数据往往具有挑战性。为了解决数据稀缺问题，我们研究了使用基于领域医学知识生成的合成数据训练深度神经网络模型的有效性。具体来说，我们针对单导联II心电图开发了一种知识驱动的高斯组合合成算法，其中每个心跳由高斯形状的P、Q、R、S和T波分量表示。使用该模拟器，我们为四种异常心电图类别生成合成数据：心房颤动、心房扑动、室性早搏和沃尔夫-帕金森-怀特综合征。我们通过使用十种不同的深度神经网络架构进行异常心电图分类来评估该合成数据的效用。结果表明，合成到真实的训练提高了四种目标异常中三种的分类性能，其中心房扑动观察到的最大架构平均增益为33.2%。进一步分析表明，合成数据带来的性能提升在真实数据集较小时更为明显。这些发现表明，基于领域知识的合成心电图可以作为有用的预训练资源，特别是在真实数据有限或难以获取的场景中。
英文摘要：
Deep Neural Networks (DNNs) typically require extensive datasets for effective training. In the medical domain, acquiring large-scale data is often challenging due to privacy concerns and the rarity of certain diseases. To address this data scarcity, we investigate the efficacy of training DNN models using synthetic data, generated based on domain-specific medical knowledge. Specifically, we develop a knowledge-driven Gaussian-composition synthesis algorithm for single-lead II ECGs, in which each heartbeat is represented by Gaussian-shaped P, Q, R, S, and T wave components. Using this simulator, we generate synthetic data for four abnormal electrocardiogram (ECG) classes: atrial fibrillation (AF), atrial flutter (AFLT), premature ventricular complex (PVC), and Wolff-Parkinson-White Syndrome (WPW). We evaluate the utility of this synthetic data by conducting abnormal ECG classification using ten different DNN architectures. Our results demonstrate that synthetic-to-real training improves classification performance for three of the four target abnormalities, with the largest architecture-averaged gain of $33.2\%$ observed for AFLT. Further analysis reveals that the performance enhancement from synthetic data is more pronounced with smaller real-world datasets. These findings suggest that domain-knowledge-based synthetic ECGs can serve as a useful pre-training resource, particularly in scenarios where real-world data are limited or difficult to obtain.
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。
