Quaternion-based CNN for heart rate prediction from PPG.

Junghwan Lee¹, Youngshin Kang², Yusang Nam², Hyuntae Lee², Danilo Mandic³, Cheolsoo Park⁴

¹The Department of Computer Engineering, Kwangwoon University, Seoul, 01897, Korea. Electronic address: hjn040281@gmail.com.

²The Department of Computer Engineering, Kwangwoon University, Seoul, 01897, Korea.

³The Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.

⁴The Department of Computer Engineering, Kwangwoon University, Seoul, 01897, Korea. Electronic address: parkcheolsoo@kw.ac.kr.

PubMed DOI

Abstract

This study aims to extract physiological information from facial images using remote photoplethysmography(rPPG), particularly heart rate, which is a critical indicator of various medical events. Existing signal processing-based rPPG algorithms and deep learning architectures have limitations in effectively processing facial video inputs under real-world conditions. To overcome these shortcomings,…

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