Investigation of PPG data from 6/1/2015 to 10/10/2023 versus five influential factors using optical physics, signal processing and VMT energy model of GH- Method: math-physical medicine (No. 944, VGT #344, 10/12/2023)

Abstract

Gerald C Hsu

On March 17, 2019, the author produced his paper number 013, which utilized optical physics and signal processing to study his postmeal glucoses (PPG) from June 1, 2015, to December 31, 2018. His recent paper, number 944, written on October 12, 2023, expands upon this research by incorporating additional five years of PPG data. The methodologies employed in this new paper include optical physics, signal processing techniques, and the viscoplastic energy model.

Drawing on his background in mathematics, physics, and engineering, the author views the biomedical data as complex, nonlinear composite signal waves. At first, he applies optical physics to establish a link between food ingredients and PPG levels. Different food colors exhibit distinct wavelengths, amplitudes, and frequencies of optical waves, which are determined by the molecular structures of the ingredients.

Following this, signal processing techniques rooted in wave theory are employed to deconstruct his collected PPG wave into 19 individual subwaveforms, each representing a single-source component. He then meticulously scrutinizes each composite signal, eventually recombining them to form a predicted PPG wave.

Additionally, he utilizes the spacedomain Viscoplastic Medicine Energy Theory (SD-VMT) to analyze the energies associated with each of the five selected influential factors for this study.

The results derived through these three distinct methods are further compared between the predicted PPG signal wave and the actual measured PPG signal wave to assess their prediction accuracies and correlations, akin to evaluating curve- shape similarities.

PDF