  Despite the recent increase in research activity, deep-learning models have
not yet been widely accepted in several real-world settings, such as medicine.
The shortage of high-quality annotated data often hinders the development of
robust and generalizable models, which do not suffer from degraded
effectiveness when presented with out-of-distribution (OOD) datasets.
Contrastive Self-Supervised Learning (SSL) offers a potential solution to
labeled data scarcity, as it takes advantage of unlabeled data to increase
model effectiveness and robustness. However, the selection of appropriate
transformations during the learning process is not a trivial task and even
breaks down the ability of the network to extract meaningful information. In
this research, we propose uncovering the optimal augmentations for applying
contrastive learning in 1D phonocardiogram (PCG) classification. We perform an
extensive comparative evaluation of a wide range of audio-based augmentations,
evaluate models on multiple datasets across downstream tasks, and report on the
impact of each augmentation. We demonstrate that depending on its training
distribution, the effectiveness of a fully-supervised model can degrade up to
32%, while SSL models only lose up to 10% or even improve in some cases. We
argue and experimentally demonstrate that, contrastive SSL pretraining can
assist in providing robust classifiers which can generalize to unseen, OOD
data, without relying on time- and labor-intensive annotation processes by
medical experts. Furthermore, the proposed evaluation protocol sheds light on
the most promising and appropriate augmentations for robust PCG signal
processing, by calculating their effect size on model training. Finally, we
provide researchers and practitioners with a roadmap towards producing robust
models for PCG classification, in addition to an open-source codebase for
developing novel approaches.
