Telemedicine data are measured directly by untrained patients, which may cause problems in data reliability. Many deep learning-based studies have been conducted to improve the quality of measurement data. However, they could not provide an accurate basis for judgment. Therefore, this study proposed a deep neural network filter-based reliability evaluation system that could present an accurate basis for judgment and verified its reliability by evaluating photoplethysmography signal and change in data quality according to judgment criteria through clinical trials. In the results, the deviation of 3% or more when the oxygen saturation was judged as normal according to each criterion was 0.3% and 0.82% for criteria 1 and 2, respectively, which was very low compared to the abnormal judgment (3.86%). The deviation of diastolic blood pressure (≥ 10 mmHg) according to criterion 3 was reduced by about 4% in the normal judgment compared to the abnormal. In addition, when multiple judgment conditions were satisfied, abnormal data were better discriminated than when only one criterion was satisfied. Therefore, the basis for judging abnormal data can be presented with the system proposed in this study, and the quality of telemedicine data can be improved according to the judgment result.
Keywords: Deep neural network; Photoplethysmography; Reliability evaluation; Telemedicine.
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