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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Real-time photoplethysmographic heart rate measurement using deep neural network filters

  • Kim, Ji Woon (Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University) ;
  • Park, Sung Min (School of Medicine, Kangwon National University) ;
  • Choi, Seong Wook (Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University)
  • Received : 2020.10.13
  • Accepted : 2021.02.02
  • Published : 2021.10.01
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Abstract

Photoplethysmography (PPG) is a noninvasive technique that can be used to conveniently measure heart rate (HR) and thus obtain relevant health-related information. However, developing an automated PPG system is difficult, because its waveforms are susceptible to motion artifacts and between-patient variation, making its interpretation difficult. We use deep neural network (DNN) filters to mimic the cognitive ability of a human expert who can distinguish the features of PPG altered by noise from various sources. Systolic (S), onset (O), and first derivative peaks (W) are recognized by three different DNN filters. In addition, the boundaries of uninformative regions caused by artifacts are identified by two different filters. The algorithm reliably derives the HR and presents recognition scores for the S, O, and W peaks and artifacts with only a 0.7-s delay. In the evaluation using data from 11 patients obtained from PhysioNet, the algorithm yields 8643 (86.12%) reliable HR measurements from a total of 10 036 heartbeats, including some with uninformative data resulting from arrhythmias and artifacts.

Keywords

Acknowledgement

This work was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (no. 2020R1F1A1073478) and the Technology Development Program (no. P0011346) funded by the Ministry of SMEs and Startups (Korea).

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