Label-free Anomaly Detection in Cardiovascular Signals with Persistence-informed Multi-instance Learning

Detecting adverse physiological events within quasi-periodic cardiovascular rhythms is clinically critical, yet existing models remain hindered by black-box reasoning and their reliance on costly expert annotations. We address this gap with a label-free, interpretable model that delivers perceptible explainability to build clinical trust and structural adaptability for interactive collaboration. We propose the Persistence-Informed Score-based Multi Instance Learning (PISMIL) framework, which synergizes motif discovery and anomaly detection by exploiting the topological dynamics inherent to quasi-periodic cardiovascular signals. PISMIL employs persistent homology to extract invariants encoding motif stability across scales, thereby producing unsupervised priors that drive pointwise anomaly scoring. A confidence-weighted pseudo-labeling strategy converts these scores into supervision signals that guide an attention-based MIL detector, yielding continuous, well-demarcated cues that facilitate a verify-then-confirm clinical workflow. Extensive validation across three real-world ECG, two real-world PPG, and two synthetic PPG datasets demonstrates that PISMIL establishes a new benchmark in anomaly detection and motif localization, surpassing well-established baselines on diverse evaluation metrics. Ablation studies further confirm that topological guidance and MIL paradigm jointly enhance decision-boundary robustness, mitigate attention collapse, and preserve discriminative structural stability. By generating anomaly maps and identifying discriminative motifs for pre-annotation, PISMIL resolves the tension between annotation burden and clinical scalability, with the potential for seamless integration into ubiquitous cardiovascular monitoring workflows.