Applying the Distributional Data Analysis Tool of Blood Pressure Density Along with the Collected Daily Data of Systolic Blood Pressure & Diastolic Blood Pressure over the past 7.5 Years from a Patient with Chronic Diseases to Investigate Heart Health Conditions Based on GH-Method: Math-Physical Medicine (No. 515)

Abstract

Gerald C Hsu

Recently, the author conducted a series of medical research projects by applying the distributional data density analysis tool on his glucose, weight, blood pressure, and heart conditions by using his collected big data regarding certain biomarkers over the multiple years. In this article, he only utilizes the collected biomarker data of blood pressure (BP) from himself, where the data covers a long time span of 7.5 years. Moreover, he can interpret the results and explore additional and deeper information, since he is most familiar with his own health conditions. The finding regarding his own body is definitely applicable to other patients. The main purpose of writing this series of research articles is to demonstrate the applicability and power of using this specific distributional data density analysis tool. In the past, when he researched certain biomarkers and their relationships with other influential factors, such as body weight, fast plasma glucose (FPG), food consumption quantity, he generally used the average values of those biomarkers. However, we know that most biomarkers, including body weight glucoses and blood pressure, would fluctuate along the time scale in the form of a “wave”. Waves have one common key factor which is the “amplitude” of this particular biomarker, where the other two key factors are frequency and wavelength. Therefore, without focusing on waveform of the biomarker and depending on its mean value, we would lose many vital, interesting, and useful hidden information. This type of mean value, such as HbA1C, or sparsely collected finger-pierced glucose or blood lipid data from quarterly lab testing can only provide partial views of health conditions. These biomarkers still have some missing information carrying certain hidden internal turmoil or vital signs, e.g. biomarker variations or its severe stimulations due to all types of external and/or internal stimulators. Therefore, by applying this basic knowledge of distributional data analysis, he has defined a new term known as the “general biomarker density or Bio-density% (BMD%)” in order to explore additional, different, deeper and useful hidden information in the collected biomarker data and their associated waveforms.

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