PPG signal reconstruction using a combination of discrete wavelet transform and empirical mode decomposition

S. K. Deric Tang, Y. Y. Sebastian Goh, M. L. Dennis Wong, Y. L. Eileen Lew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Photoplethysmographic (PPG) signals, which are measured by pulse oximeter embedded in a form of wristband, are typically used for measuring heart rates. Such wearable sensors may be used for early detection of abnormal conditions for preventive actions in monitoring individual health. However, it is challenging to estimate heart rates using PPG signals with high accuracy due to the irregular motion artifacts, thus making the estimation of heart rate unreliable. In this paper, we proposed the use of Empirical Mode Decomposition (EMD) followed by Discrete Wavelet Transform (DWT) for noise reduction of the PPG signals. We calculated the heart beat rate per minute (BPM) from the reconstructed PPG signals and evaluated the performance of the proposed method in terms of Absolute Maximum Error (AME) and Mean Sum Error (MSE) with the provided ground-truth BPM computed from ECG signals. We have shown an improvement in the MSE values from 67% of the datasets used in this study. We also analyzed the relationship between the performances obtained based the level of movement intensity which are measured using the accelerometer.

Original languageEnglish
Title of host publication2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)
PublisherIEEE
ISBN (Electronic)9781509008452
DOIs
Publication statusPublished - 19 Jan 2017
Event6th International Conference on Intelligent and Advanced Systems 2016 - Kuala Lumpur, Malaysia
Duration: 15 Aug 201617 Aug 2016

Conference

Conference6th International Conference on Intelligent and Advanced Systems 2016
Abbreviated titleICIAS 2016
CountryMalaysia
CityKuala Lumpur
Period15/08/1617/08/16

Keywords

  • BPM
  • DWT
  • ECG
  • EMD
  • PPG

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Artificial Intelligence
  • Computer Science Applications

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