ST-AuthNet: A Spatiotemporal Attention-Driven Lightweight ECG Biometric Authentication System

Amidst the rapid integration of Medical Internet of Things (MIoT) into health monitoring ecosystems, electrocardiogram (ECG)-based biometric authentication has emerged as a pivotal component in securing smart healthcare architectures, leveraging its inherent biological uniqueness and real-time monitoring capabilities. Current ECG authentication methodologies face three MIoT-specific challenges: 1) Conventional feature extraction struggles with spatial heterogeneity in multi-device 12-lead signals; 2) Single-cycle analysis lacks generalizability across physiological states; 3) Environmental noise degrades edge computing robustness. To address these limitations, this study proposes ST-AuthNet, a lightweight ECG authentication framework that synergistically integrates spatiotemporal attention mechanisms with enhanced residual networks. First, we redesign the ResNet residual block architecture by replacing conventional 1× 1 convolutional downsampling with hybrid 2× 2 average pooling and 1× 1 convolutional operations, effectively mitigating low-amplitude morphological feature loss (e.g., P/T waves) during feature map compression. Next, a multi-head cross-attention mechanism is introduced to dynamically capture inter-lead spatial correlations and intra-PQRST temporal dependencies across ECG waveforms. Finally, an adaptive threshold decision module is developed to optimize model robustness against physiological variability and environmental perturbations through dynamic classification boundary adjustment. Evaluations demonstrate state-of-the-art performance with 99.77% (CYBHI), 88.60% (MIT), 76.33% (MIT2), and 92.44% (HeartID-V) accuracy, significantly outperforming existing methods in cross-scenario biometric verification.