Evaluation of Data Augmentation Under Label Scarcity for ECG-Based Detection of Sleep Apnea
Year: 2025
Type: article
Abstract: Supervised ECG-based sleep apnea detection typically depends on large and fully annotated datasets, yet the rarity and cost of labeling apneic events often lead to substantial annotation scarcity in practice. This study provides a controlled evaluation of how such scarcity degrades classification performance and, as a key contribution, investigates whether a constrained, morphology-preserving ECG augmentation framework can compensate for reduced apnea-label availability. Using the PhysioNet Apnea–ECG dataset, we simulated seven levels of label retention (r=5–100%) and trained a lightweight CNN–BiLSTM model under both subject-dependent (SD) and subject-independent (SI) five-fold protocols. Offline augmentation was applied only to apnea segments and consisted of simple, physiologically motivated time-domain perturbations designed to retain realistic cardiac and respiratory dynamics. Across both evaluation settings, augmentation substantially mitigated performance loss in the low- and mid-scarcity regimes. Under SI evaluation, the mean F1-score improved from 0.57 to 0.72 at r=5% and from 0.63 to 0.76 at r=10%, with scores at r=10–40% (0.75–0.77) approaching the full-label baseline of 0.79. Temporal and spectral analyses confirmed preservation of P–QRS–T morphology and respiratory modulation without distortion. These results demonstrate that simple and interpretable ECG augmentations provide an effective and reproducible baseline for data-efficient apnea screening and offer a practical path toward scalable annotation and robust single-lead deployment under label scarcity.
Source: Applied Sciences
DOI: 10.3390/app152413231
Authors: Semin Ryu, Jeonghwan Koh, In cheol Jeong
Institutions: Hallym University, Icahn School of Medicine at Mount Sinai
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G489919892
OpenAlex ID: W4417436145