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Development of a real‐time adaptive delta compression algorithm for photoplethysmography system
Author(s) -
Chong Kim Soon,
Gan Kok Beng,
Zahedi Edmond
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22712
Subject(s) - photoplethysmogram , computer science , data compression , real time computing , data acquisition , bluetooth , signal (programming language) , root mean square , interface (matter) , algorithm , computer hardware , simulation , wireless , computer vision , engineering , filter (signal processing) , electrical engineering , telecommunications , parallel computing , bubble , maximum bubble pressure method , programming language , operating system
Photoplethysmography (PPG) is a simple and low‐cost optical technique that measures the blood volume change of the microvascular bed. The multichannel PPG recording system enables researchers to study the characteristics of the PPG signal from multiple body sites. Long‐time, continuous PPG signal monitoring helps doctors in medical diagnosis and treatment. However, the development of the PPG signal acquisition system is limited by storage capacity and computational and hardware capability. Limitation in storage capacity and communication efficiency can be addressed by introducing data compression algorithms. In this paper, a single‐channel, high‐resolution (24‐bit) wireless PPG data acquisition system using Bluetooth connectivity with the adaptive delta compression algorithm is presented. A graphical user interface was developed using MATLAB to interface with the system using a serial port profile. The PPG signals were decompressed and stored in the local hard drive for future analysis. The performance of adaptive data compression algorithm was evaluated with 12 healthy subjects. Results show that the compression ratio achieved is 41.5. The mean percentile root‐mean‐square (RMS) difference at the sampling frequency of 100 Hz is 0.13%. The mean normalized percentile RMS difference value is 9.78%. The system was successfully tested in a continuous acquisition mode for up to 60 min. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.