Reduced complexity algorithm for heart rate monitoring from PPG signals using automatic activity intensity classifier. (July 2019)

• Record Type: Journal Article
• Authors: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 293
• Page End: 301
• Publication Date: 2019-07
• Subjects: Heart rate monitoring -- Photoplethysmography (PPG); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.04.026
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Heart rate estimation from wrist-mounted photoplethysmographic sensors. Geometric separation of motion artifacts helps clean the useful signal. Activity intensity detection helps optimize heart rate tracking. Genetic algorithms can optimize tracking parameters for better accuracy. Abstract: Photoplethysmography (PPG) is a well-studied and promising technique to detect heart rate (HR) using cheap, non-invasive, wrist-wearable sensors that sense the amount of light reflected by the skin, related to the blood flow beneath. Still, the main issue is the high sensitivity to motion, which produces severe artifacts in the signal, often impeding accurate HR tracking. In this paper we present a method that combines an automatic activity intensity classifier, to select the proper amount of artifact cleaning that needs to be performed on the signal, with a geometric-based signal subspace approach to estimate the HR component of the PPG signal. Experimental evaluation is performed over a widely available dataset and the results are compared to an ECG-derived golden standard.