PULSE: A personalized physiological signal analysis framework via unsupervised domain adaptation and self-adaptive learning

Abstract

Despite the remarkable success of artificial intelligence (AI) in physiological signal analysis, the inherent variability between individuals poses significant challenges to model generalization. Existing personalization approaches typically rely on supervised fine-tuning of pre-trained general models (GMs) using labeled data from unseen subjects, which limits their practical deployment due to labeling costs and scalability issues. To address this challenge, we propose PULSE, a personalized unsupervised domain adaptation framework that enhances model generalization through self-adaptive learning. Our approach incorporates three key components: (1) an Adaptive Channel Selection and Embedding (ACSE) module that optimizes multi-channel signal processing through learnable attention mechanisms, (2) an Embedding-guided Representation Learning (ERL) strategy that enhances intra-class feature consistency during GM pre-training, and (3) a Self-adaptive Pseudo-label Enhancement (SPE) method that generates high-quality pseudo-labels to facilitate alignment between inter-domain data distributions during GM fine-tuning. Extensive experiments on large-scale physiological datasets, including cross-database validation, demonstrate that PULSE achieves 2.8%-6.5% improvements in F1 score (from 91.5% to 95.2 to 98.0%) and 2.6%-6.4% improvements in accuracy (from 90.8% to 94.6% to 97.2%). The framework’s effectiveness is validated through dynamic electrocardiogram analysis, showcasing its potential for broader applications in physiological signal processing. The code is publicly available at https://github.com/fdu-harry/PULSE.