Math-Physical Medicine

NO. 110

Comparison of glucose results from Finger-piercing and Sensor-collected data using GH-Method: math-physical medicine

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

This paper addresses the varying glucose results between Finger-piercing and test strip (Finger) and a continuous monitored blood glucose (CMBG) device (Sensor).  It covers mainly postprandial plasma glucose (PPG) but also deals with fasting plasma glucose (FPG).  The author uses his own type 2 diabetes (T2D) metabolic conditions control as a case study for some detailed illustration and explanation of this methodology.

Math-physical medicine starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

The glucose data are collected during the period of 483 days (5/5/2018 through 8/30/2019).  Sensor data are collected at 74 times per day with a total of 35,842 sensor data.  Finger data are collected at four times per day with a total of 1,932 finger data.

Each day has one sensor FPG waveform from 00:30 midnight to 7:45 am and three sensor PPG waveforms covering a three-hour period.  There are a total of 483 FPG waveforms and 1,449 PPG waveforms.

Each PPG waveform has five key data points:

  • Open glucose at 0 minute
  • Maximum glucose (usually around 60 minutes)
  • Minimum glucose
  • Close glucose (at 180 minutes)
  • Average glucose

Table 1 (summarized from Figure 1 through 6) are self-explanatory.  The key findings are re-stated below:

  1. The max sensor PPG is 41% higher than max finger PPG.
  2. The averaged sensor PPG is 13% higher than averaged Finger PPG.
  3. The averaged Sensor PPG at 120 minutes is 18% higher than Finger PPG at 120 minutes (Figure 4).
  4. 32% of total sensor PPG values belong to the “high-glucose” segments (both >140 mg/dL and >180 mg/dL) which have separated higher percentages (26% and 4%), than Finger PPG values.  These high-glucose segments must be controlled tightly in order to reduce their damages on internal organs via their associated energies.
  5. 0.2% of total sensor PPG values belongs to the “low-glucose” segment (<70 mg/dL) which is slightly lower than Finger’s low-glucose segment percentage (0.3%).  These two extremely low glucoses (averaged 65 mg/dL for sensor and 68 mg/dL for finger) can cause insulin shock and be life-threatening.
  6. PPG wave rises at 58 mg/dL per hour to reach to its maximum point and then decays at 28 mg/dL per hour until reach to its waveform’s close point.
  7. Glucoses of different categories within Sensor database have high correlation among them.  However, there are low correlations between Finger and Sensor, except sensor’s 120 minutes and finger’s two-hour have a 50% correlation.
Table 1: Comparison of Sensor and Finger Glucoses
Figure 1: Comparison of Sensor and Finger Glucoses
Figure 2: Detailed Glucoses of Sensor and Finger
Figure 3: Detailed Sensor data
Figure 4: Comparison between Finger PPG and Sensor PPG at 120 minutes
(18% difference)
Figure 5: 30-days moving averaged glucose curves to calculate Correlations.
Figure 6: Waveforms of FPG & PPG (Correlation -87%)

First, observation from combined sensor and finger glucose values could offer a reasonable range (13% to 41%) of a more realistic glucose situation for a T2D patient.

Second, if healthcare professionals could study and understand glucose with such a depth and detailed manner as shown in this paper, then their knowledge and ability to assist diabetes patients to control their complications would be greatly improved.


  1. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
  2. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  3. Hsu, Gerald C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
  4. Hsu, Gerald C. (2018, June). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions. Video presented at the meeting of the 3rd International Conference on Endocrinology and Metabolic Syndrome 2018, Amsterdam, Netherlands.
  5. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.