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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 6812404, 8 pages
https://doi.org/10.1155/2018/6812404
Research Article

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

1Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
2Northern Medical Physics and Clinical Engineering, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK

Correspondence should be addressed to John Allen; ku.shn.htun@nella.nhoj

Received 4 August 2017; Accepted 31 December 2017; Published 29 January 2018

Academic Editor: Chung-Min Liao

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.

Abstract

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.