Face-Based Heart Rate Signal Decomposition and Evaluation Using Multiple Linear Regression

Kuan Yi Lin, Duan Yu Chen, W. J. Tsai

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Contact measurements of the cardiac pulse using the conventional electrocardiogram equipment requires patients to wear adhesive gel patches or chest straps that can cause skin irritation and discomfort. Commercially available pulse oximetry sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can be painful to use. Therefore, a novel robust non-contact technique is developed for the evaluation of heart rate variation. According to the periodic variation of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as heartbeats cause changes to blood volume in the blood vessels in the face, a reflectance signal is decomposed from consecutive frames of the green channel of the facial region. Furthermore, ensemble empirical mode decomposition of the Hilbert-Huang transform (HHT) is used to acquire the primary heart rate signal while reducing the effect of ambient light changes. The effective instantaneous frequencies from intrinsic mode functions decomposed by HHT are implemented by the multiple-linear regression model to evaluate heart rates before the frequencies were rectified by maximum likelihood method assuming Poisson distribution, and the minimum elapse time for heart rate evaluation is also evaluated in the estimate process. Experimental results show that our proposed approach provides a convenient non-contact method to evaluate heart rate and outperforms the current state-of-the-art method with higher accuracy and smaller variance.

Original languageEnglish
Article number7327119
Pages (from-to)1351-1360
Number of pages10
JournalIEEE Sensors Journal
Volume16
Issue number5
DOIs
StatePublished - 1 Mar 2016

Keywords

  • APPL
  • EDICS

Fingerprint Dive into the research topics of 'Face-Based Heart Rate Signal Decomposition and Evaluation Using Multiple Linear Regression'. Together they form a unique fingerprint.

Cite this