Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

EURASIP Journal on Advances in Signal Processing

Shahid Ismail¹  Imran Siddiqi¹  Usman Akram² 

[1] Bahria University;National University of Sciences and Technology;

关键词: PPG;    Heart rate;    Machine learning;    Motion artifacts;   

DOI  :  10.1186/s13634-020-00714-2

来源: DOAJ

【 摘 要 】

Abstract Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

【 授权许可】

Unknown