Empowering cardiovascular diagnostics with SET-MobileNet: A lightweight and accurate deep learning based classification approach

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating early detection and accurate diagnosis for improved patient outcomes. This study introduces SET-MobileNet, a lightweight deep learning model designed for automated heart sound classification, integrating transformers to capture long-range dependencies and squeeze-and-excitation (SE) blocks to emphasize relevant acoustic features while suppressing noise artifacts. Unlike traditional methods that rely on handcrafted features, SET-MobileNet employs a multimodal feature extraction approach, incorporating log-mel spectrograms, Mel-Frequency Cepstral Coefficients (MFCCs), chroma features, and zero-crossing rates to enhance classification robustness. The model is evaluated across multiple publicly available heart sound datasets, including CirCor, HSS, GitHub, and Heartbeat Sounds, achieving a state-of-the-art accuracy of 99.95% for 2.0-second heart sound segments in the CirCor dataset. Extensive experiments demonstrate that multimodal feature representations significantly improve classification performance by capturing both time-frequency and spectral characteristics of heart sounds. SET-MobileNet is computationally efficient, with a model size of 8.61 MB and single-sample inference times under 6.5 ms, making it suitable for real-time deployment on mobile and embedded devices. Ablation studies confirm the contributions of transformers and SE blocks, showing incremental improvements in accuracy and noise suppression.