Boosting ECG Classification Performance by Pre-training with Synthesized Data

arXiv:2606.10802v1 Announce Type: new Abstract: 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,

Source: arXiv cs.LG — read the full report at the original publisher.

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