Open AccessRespiratory rate extraction based on plethysmographic wave analysis
Author(s) -
Sugondo Hadiyoso,
Ervin Masita Dewi,
Tati L. R. Mengko,
Hasballah Zakaria
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/830/3/032050
Subject(s) - respiratory rate , plethysmograph , hilbert–huang transform , computer science , respiratory system , breathing , fast fourier transform , frequency domain , medicine , heart rate , biomedical engineering , acoustics , cardiology , anesthesia , filter (signal processing) , algorithm , physics , blood pressure , computer vision
Respiratory rate (RR) is one of the vital parameters of a person’s health. In certain conditions such as patients with respiratory problems or heart who are in the treatment room, respiratory rate monitoring is carried out continuously. In health care centers such as hospitals, RR measurements use a bed side monitor. However, this device is not yet widely available in small scale health services such as Community Health Centers (Puskesmas) because this device is expensive. One alternative for RR extraction is through plethysmographic wave analysis measured using a photopletismograph (PPG) device. This is based on the hypothesis that breathing affects the dynamics of PPG wave amplitude modulation so that by extracting this information we can know the respiratory rate. PPG devices are widely available in small health services because of their low prices. Meanwhile, PPG is only used to measure heart rate and oxygen saturation. In this study, a method for extracting RR information is proposed by analyzing PPG waves. Two methods, namely empirical mode decomposition (EMD) and variational mode decomposition (VMD), are applied to obtain respiratory oscillation information. The aim is to get the most optimum method for RR extraction. The performance test of the two proposed methods was carried out through time-frequency domain transformation using Fast Fourier Transform. The method used in this study is expected to be applied to conventional PPG devices.