Journal
SENSORS
Volume 21, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/s21062188
Keywords
convolutional neural network; mobile healthcare; photoplethysmogram; recurrence plot; signal quality assessment
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF-2018R1A4A1025704]
- Bio & Medical Technology Development Program of the NRF - Korean government, MSIP, Republic of Korea [NRF-2016M 3A9F1941328]
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This study successfully developed a machine learning model that accurately evaluates the quality of a photoplethysmogram. Through data expansion, it achieved high accuracy in signal quality assessment without requiring complicated pre-processing or feature detection.
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 complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min 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 'poor' classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. 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 'poor' class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. 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 complicated pre-processing or a feature detection process.
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