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.
|Number of pages||8|
|Journal||Computational and Mathematical Methods in Medicine|
|Publication status||Published - 29 Jan 2018|
Bibliographical noteCopyright © 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.
Waugh, W., Allen, J., Wightman, J., Sims, A. J., & Beale, T. A. W. (2018). Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography. Computational and Mathematical Methods in Medicine, 2018, . https://doi.org/10.1155/2018/6812404