Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis



The presence of motion artifacts in photoplethysmographic (PPG) signals
is one of the major obstacles in the extraction of reliable cardiovascular
parameters in continuous monitoring applications. In the current paper we
present an algorithm for motion artifact detection based on the analysis of the
variations in the time and the period domain characteristics of the PPG signal.
The extracted features are ranked using a normalized mutual information
feature selection algorithm and the best features are used in a support vector
machine classification model to distinguish between clean and corrupted
sections of the PPG signal. The proposed method has been tested in healthy and
cardiovascular diseased volunteers, considering 11 different motion artifact
sources. The results achieved by the current algorithm (sensitivity—SE:
84.3%, specificity—SP: 91.5% and accuracy—ACC: 88.5%) show that
the current methodology is able to identify both corrupted and clean PPG
sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular
diseases (ACC: 89.5%) context.


Clinical Informatics


Physiological Measurement, Vol. 35, pp. 2369-2388, November 2014

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