Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Mar 29, 2021)
Date Submitted: Dec 10, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Recurrence plot and machine learning for signal quality assessment of photoplethysmogram in mobile environment
ABSTRACT
Background:
In clinical use of photoplethysmogram, waveform distortion due to motion noise or low perfusion may lead to inaccurate analysis and diagnostic results. Therefore, it is necessary to find an appropriate analysis method to evaluate the signal quality of the photoplethysmogram so that its wide use in mobile healthcare can be further increased.
Objective:
The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring a complicated pre-processing. Its performance was then verified.
Methods:
Photoplethysmograms were recorded for 76 participants (5 minutes for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'bad' classes and converted to a two-dimensional phase space trajectory image with size of 124 × 124 using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. It was verified through a five-fold cross validation.
Results:
As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘bad’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994.
Conclusions:
The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require a complicated pre-processing or feature detection process. Clinical Trial: KCT0002080
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