The novel PHVA framework outperformed state-of-the-art anomaly detection baselines for ventricular arrhythmia detection by up to 8.7% in AUC across three public ECG datasets.
Does the PHVA framework improve the detection of ventricular arrhythmias compared to state-of-the-art baselines in ECG datasets?
A novel anomaly detection framework (PHVA) using self-supervised pseudo-anomaly generation and hard-sample mining improves ventricular arrhythmia detection on ECG by up to 8.7% in AUC compared to baseline models.
Ventricular arrhythmias (VA) are among the most prevalent and clinically significant cardiac arrhythmias. Conventional detection methodologies predominantly employ supervised learning approaches that depend on precisely annotated training datasets. However, the morphological similarity between VA waveforms and noise artifacts poses a significant challenge for traditional algorithms in discriminating these clinically distinct categories. In this paper, we propose a novel anomaly detection framework named PHVA (Pseudo-data and Hard-sample mining for VA detection) based on one-class anomaly detection, where only normal ECGs are used for training, while pseudo-anomalies are generated through self-supervised modules. These modules, including pseudo-anomaly generation and hard-sample mining, provide supervisory signals without requiring abnormal labels. Specifically, we introduce a physiology-aware synthetic ECG generation method that captures the beat morphology and waveform characteristics of VA while incorporating realistic noise simulations based on conventional noise models. These pseudo-annotated signals are then used to refine the decision boundary of a time-frequency hypersphere, constructed from normal ECG features in both temporal and spectral domains. Additionally, we employ triplet-loss-based hard-sample mining to improve the model's discriminative power for ventricular fibrillation detection. Extensive experiments on three public ECG datasets demonstrate that our proposed method PHVA achieves superior overall performance, outperforming state-of-the-art anomaly detection baselines by up to 8.7% in AUC (the Area Under a Receiver Operating Characteristic Curve). We share an online repository containing the code and data processing scripts https://github.com/Augustgaoshaochen/PHVA.
Fan et al. (2026) studied this question. The novel PHVA framework outperformed state-of-the-art anomaly detection baselines for ventricular arrhythmia detection by up to 8.7% in AUC across three public ECG datasets.