Open AccessReal‐time photoplethysmographic heart rate measurement using deep neural network filters
Author(s) -
Kim Ji Woon,
Park Sung Min,
Choi Seong Wook
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2020-0394
Subject(s) - photoplethysmogram , artificial intelligence , artificial neural network , computer science , waveform , noise (video) , pattern recognition (psychology) , deep neural networks , measure (data warehouse) , computer vision , filter (signal processing) , data mining , telecommunications , radar , image (mathematics)
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.