Predict PPG Values of COVID Period (using the pre-COVID data as the baseline) Applying the Higher Order Equations of Interpolation Perturbation Theory from Quantum Mechanics and Carbs/Sugar Intake Amount as the Perturbation Factor Based on GH-Method: Math-Physical Medicine (No. 462)

Abstract

Gerald C Hsu

In this research note, the author applies the methodology of higher-order interpolation perturbation theory from quantum mechanics on his medical research work. This perturbation theory application includes the first-order, second-order, and third-order, to generate three predicted PPG waveforms with different prediction accuracies. He then collects two separate measured postprandial plasma glucose (PPG) data and their synthesized waveforms generated for two periods, pre-COVID (5/5/2018 - 1/18/2020) and COVID (1/19/2020 - 6/7/2021), as two baselines for comparison between predicted PPG data and waveforms (using pre-COVID as the baseline) and the measured COVID PPG data and waveform. There are two final yardsticks to check in this study. The first target is to verify the prediction accuracies of these three perturbed PPG values. The second target is to examine the waveform similarity via calculated correlation coefficients between the measured PPG dataset or waveform and the three perturbed PPG datasets or waveforms. The main purpose is to examine the prediction accuracy and waveform similarities in his current or future period of glucoses by using three different orders of perturbation equations based on the glucose data from the previous period as the prediction baseline.

PDF