Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography

William Waugh, John Allen, James Wightman, Andrew J. Sims, Thomas A. W. Beale

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
8 Downloads (Pure)


Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.

Original languageEnglish
Article number6812404
Number of pages8
JournalComputational and Mathematical Methods in Medicine
Publication statusPublished - 29 Jan 2018
Externally publishedYes

Bibliographical note

Copyright © 2018 William Waugh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Dive into the research topics of 'Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography'. Together they form a unique fingerprint.

Cite this