TopSeg: A Multi-Scale Topological Framework for Data-Efficient Heart Sound Segmentation

TL;DR

TopSeg introduces a topological framework for heart sound segmentation that enhances data efficiency and generalization, outperforming traditional methods especially with limited labeled data.

Contribution

The paper presents TopSeg, a novel topological representation-based approach that improves heart sound segmentation accuracy and robustness with minimal training data.

Findings

TopSeg outperforms spectrogram and envelope-based inputs at low data budgets.

Combining H_0 and H_1 topological features improves boundary stability.

TopSeg achieves superior cross-dataset performance with limited training data.

Abstract

Deep learning approaches for heart-sound (PCG) segmentation built on time-frequency features can be accurate but often rely on large expert-labeled datasets, limiting robustness and deployment. We present TopSeg, a topological representation-centric framework that encodes PCG dynamics with multi-scale topological features and decodes them using a lightweight temporal convolutional network (TCN) with an order- and duration-constrained inference step. To evaluate data efficiency and generalization, we train exclusively on PhysioNet 2016 dataset with subject-level subsampling and perform external validation on CirCor dataset. Under matched-capacity decoders, the topological features consistently outperform spectrogram and envelope inputs, with the largest margins at low data budgets; as a full system, TopSeg surpasses representative end-to-end baselines trained on their native inputs under the same budgets while remaining competitive at full data. Ablations at 10% training confirm that all scales contribute and that combining H_0 and H_1 yields more reliable S1/S2 localization and boundary stability. These results indicate that topology-aware representations provide a strong inductive bias for data-efficient, cross-dataset PCG segmentation, supporting practical use when labeled data are limited.