Fuzzy-genetic photoplethysmograph peak detection

Author

Canlas, Reich Rechner D. ; Ochotorena, Carlo Noel E. ; Dadios, Elmer P.

Author_Institution

De La Salle Univ., Manila, Philippines

fYear

2014

fDate

12-16 Nov. 2014

Firstpage

1

Lastpage

4

Abstract

Photoplethysmography (PPG) promises noninvasive body metrics measurement, especially that of heart rate. However, this system is prone to noise due to motion artifacts. This paper presents a fuzzy inference system, with membership functions and rules tuned by a genetic algorithm that utilizes the principal components of the PPG data accelerometer data from the x, y, and z coordinates in order to recover the peaks from the distorted PPG signal. A comparative test demonstrated that a 56.66% peak-to-peak correspondence to a reference ECG signal is achievable with the fuzzy-genetic system in place.

Keywords

accelerometers; electrocardiography; fuzzy reasoning; genetic algorithms; medical signal processing; photoplethysmography; principal component analysis; signal denoising; PPG data accelerometer data; distorted PPG signal; fuzzy inference system; fuzzy-genetic photoplethysmograph peak detection; genetic algorithm; heart rate; membership functions; motion artifacts; noise; noninvasive body metrics measurement; peak-to-peak correspondence; principal components; reference ECG signal; x coordinates; y coordinates; z coordinates; Conferences; Electrocardiography; Fuzzy logic; Genetic algorithms; Noise; Principal component analysis; Standards; ECG; Fuzzy Logic; Genetic Algorithm; Motion Artifact Noise; PPG; Photoplethysmography; Signal Processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014 International Conference on

Conference_Location

Palawan

Print_ISBN

978-1-4799-4021-9

Type

conf

DOI

10.1109/HNICEM.2014.7016243

Filename

7016243