应用量子力学一阶摄动理论预测和构建餐后血糖波形(gh -方法:数学-物理医学)

Gerald C. Hsu
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引用次数: 5

摘要

最初,他用2015/5/12 /13/2019期间收集的23,725 PPG传感器数据应用分割模式分析了他的1825顿饭。最初,他的两个细分是基于餐后碳水化合物/糖摄入量的“第一因素”和餐后步行步数的“第二因素”。低碳水化合物餐约占总餐数的2/3(1209餐,每餐8.5克),高碳水化合物餐约占总餐数的1/3(615餐,每餐27.1克)。一个标准波形(曲线)包含每条PPG曲线的13个数据点和每15分钟时间段的一个输入数据。他的餐后步行步数相当(4238步对4282步)。因此,他决定只关注第一个因素碳水化合物/糖的摄入量。
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Applying First-Order Perturbation Theory of Quantum Mechanics to Predict and Build a Postprandial Plasma Glucose Waveform (GH-Method: Math- Physical Medicine)
Initially, he applied segmentation pattern analysis to analyze his 1,825 meals with 23,725 PPG Sensor data collected during a period of 5/5/201812/13/2019. Initially, his two segments were based on both “first factor” of meal’s carbs/sugar intake amounts and “second factor” of post-meal walking steps. His low-carb meals occupy about 2/3 of the total meals (1,209 meals with 8.5 grams per meal) and high-carb meals occupy about 1/3 of the total meals (615 meals with 27.1 grams per meal). A standard waveform (curve) contains 13 data points for each PPG curve and one input data for each 15-minute time segment. His postmeal walking steps are comparable (4,238 vs. 4,282 steps). Therefore, he decided to focus on the first factor of carbs/sugar intake amount only.
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