{"type":"ScholarlyArticle","title":"DNA-PPG: A Foundation Model for Photoplethysmography via Dual Neighborhood Alignment","authors":[{"name":"Yizhang Yang","slug":"yizhang-yang","given":"Yizhang","family":"Yang","affiliation":""},{"name":"Jinshi Cui","slug":"jinshi-cui","given":"Jinshi","family":"Cui","affiliation":""},{"name":"Wenlong Wu","slug":"wenlong-wu","given":"Wenlong","family":"Wu","affiliation":""},{"name":"Chunlong Tu","slug":"chunlong-tu","given":"Chunlong","family":"Tu","affiliation":""},{"name":"Yang Zhang","slug":"yang-zhang","given":"Yang","family":"Zhang","affiliation":""},{"name":"Junshi Lu","slug":"junshi-lu","given":"Junshi","family":"Lu","affiliation":""},{"name":"Li Wang","slug":"li-wang","given":"Li","family":"Wang","affiliation":""},{"name":"Guosong Gao","slug":"guosong-gao","given":"Guosong","family":"Gao","affiliation":""}],"abstract":"Existing physiological foundation models face two limitations: rigid hard-negative sampling indiscriminately repels morphologically similar samples, distorting the natural manifold; and coarse discretization strategies sever the intrinsic continuity of physiological states, inducing precision loss. To address these challenges, we propose DNA-PPG, a novel pre-training framework anchored in Dual Neighborhood Alignment. DNA-PPG integrates the Morphology-Aware Self-Supervised Branch using Time-Frequency Soft Weighting to capture universal signal dynamics shared among diverse subjects, with the Physiological Semantic Alignment Branch that projects physiological indicators into continuous semantic space to explicitly embed precise physiological priors into the representation space. We scale the pre-training to 10.7 million PPG segments from over 8,400 subjects to ensure robust generalization. Extensive evaluations on six downstream benchmarks demonstrate that DNA-PPG significantly outperforms state-of-the-art baselines, achieving an 18% reduction in regression error and an 11.5% improvement in classification performance. These results validate DNA-PPG as a robust, universal feature extractor for  diverse photoplethysmography applications.","doi":"10.24963/ijcai.2026/777","url":"https://ijcai.org/proceedings/2026/777","pdf":"https://ijcai.org/proceedings/2026/0777.pdf","markdown":"https://ijcai.org/proceedings/2026/777.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/777","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":777,"pages":{"first":6984,"last":6992},"keywords":["Self-supervised learning: Self-supervised learning","Remote monitoring: Remote monitoring","Health data mining: Health data mining"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}