A Knowledge-Guided Feature Fusion and Fuzzy Decision Integration Model for ECG-Based Cardiovascular Disease Diagnosis

Accurate and interpretable electrocardiogram (ECG) analysis is critical for diagnosing cardiovascular diseases (CVDs). While deep learning models have achieved remarkable success in ECG classification, their reliance on data-driven feature extraction often compromises interpretability, making their diagnostic decisions less transparent and clinically meaningful. Motivated by this gap, we present a knowledge-guided feature fusion and fuzzy decision integration model that enhances both classification performance and interpretability. Our approach begins by extracting temporal-frequency statistical features from ECG signals, incorporating clinically relevant characteristics that purely data-driven models frequently overlook. To enrich feature interactions and improve signal representation, we integrate a bidirectional cross-attention mechanism that aligns ECG signals with temporal-frequency statistical features. To further enhance decision reliability, we introduce a fuzzy integral-based decision fusion strategy that aggregates outputs from multiple classifiers. Specifically, we utilize the Choquet integral to model varying classifier contributions, ensuring more stable and reliable predictions. Experiments on three public ECG datasets demonstrate that our method outperforms state-of-the-art models, highlighting its potential for reliable and interpretable CVDs diagnosis in clinical practice.