Topological Representations Enhance Data-Efficient Heart Sound Segmentation

Global: Topological Representations Enhance Data-Efficient Heart Sound Segmentation

A new framework called TopSeg, developed by researchers at an unnamed institution, leverages multi‑scale topological features to improve phonocardiogram (PCG) segmentation when labeled data are scarce. The approach was detailed in a preprint posted to arXiv on October 25, 2025, and it aims to provide more robust segmentation for clinical and wearable applications.

Methodological Overview

TopSeg encodes the dynamics of heart‑sound recordings using topological descriptors that capture shape information across scales. These features are then processed by a lightweight temporal convolutional network (TCN), followed by an inference step that enforces order and duration constraints on the predicted heart‑sound events.

Training Regime and Datasets

The authors trained the model exclusively on the PhysioNet 2016 dataset, applying subject‑level subsampling to simulate low‑data conditions. External validation was performed on the CirCor dataset, allowing assessment of cross‑dataset generalization.

Performance Relative to Conventional Inputs

When paired with decoders of comparable capacity, the topological inputs consistently outperformed traditional spectrogram and envelope representations. The advantage was most pronounced at limited data budgets; for example, with only 10% of the training data, TopSeg achieved higher S1/S2 localization accuracy and more stable boundary detection than baseline models.

Ablation Findings

Ablation experiments confirmed that each topological scale contributed to overall performance. Moreover, combining the zeroth‑order (H₀) and first‑order (H₁) features yielded the most reliable segmentation outcomes, reinforcing the value of multi‑scale topology.

Implications and Future Directions

These results suggest that topology‑aware representations act as a strong inductive bias, enabling data‑efficient heart‑sound analysis that can generalize across datasets. The authors propose that such representations could facilitate practical deployment of PCG segmentation tools in settings where expert‑annotated recordings are limited.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.