臺灣博碩士論文加值系統

Background: Nowadays, the tools and methods to measure Blood Glucose Levels (BGLs) are quite, invasive, and inconvenient, especially for diabetes patients. For these reasons, several studies are currently focusing on predicting and measuring the glycaemia using instruments such as near infrared spectroscopy (NIR) and Raman spectroscopy. Recently, a few studies tried to use the electrocardiogram (ECG) and its multiple features to establish a link between them and the blood glucose levels in order to predict the glycaemia without the need to prick the skin. The techniques used rely mostly on the extraction of a few segments and intervals from the ECG signals of patients, that have in parallel, their BGLs monitored continuously to establish a linear regression between some of these ECG parameters and the glycaemia.
Aims: This research relies on data (ECG and BGLs) obtained from Singular Wings Medical (SWM) company. The purpose is to use the multiresolution wavelet approach to extract heart rate (HR) parameters-related information of the ECG signals and link them to BGLs to analyse in which aspects the Blood Glucose (BG) and ECG are linked together. Research around the ECG and its link with BG could have an impact on the thought and design of a potential device that could measure glycemia from one or more of its features.
Material and methods: Two studies focused on following and analysing the HR and BG variations per days for several candidates, for which the information have been provided by SWM. The BG data, measured by the Freestyle Libre 2, were sampled at 1 data every 15 min and were directly accessible with excel files provided by SWM. The ECG sampled at 250 Hz and as 10 seconds signals were provided as srj files and processed under MATLAB software. The HR information was retrieved from 2 methods: using Wavelet transform (WT) with MATLAB software and using SWM calculations as a comparison and validation of the HR obtained from the WT.
The third study focused on correlation tests, with Thonny software (Python), between statistical parameters (mean, median, mode, standard deviation and variance) of BG and HR calculated previously with MATLAB.
To go further, the possibility of putting an ECG-BG device on the french market has been analysed through different tools, essentially to collect diabetic population needs and bring out recommendations on how to enter the market. This has been done through a questionnaire, a benchmarking and a swot analysis.
Results: Results from two first study showed a trend of the HR variations following the ones from BG. That’s to say when the BG increases, the HR tends to rise too and vice-versa. Correlation tests also showed that the median BG and mean HR, median BG and mode HR and mode BG and mode HR have positive linear relationship between each other, with respective correlation coefficients of 0,75998, 0,72 and 0,7122.
The market analysis highlighted the major key players (Abbott, Medtronic and Dexcom) in the Blood Glucose Monitoring (BGM) devices market as well as the strong points making their devices widely used among others. Also, the questionnaire has been filled by 155 diabetic persons that have high hopes for improvements of future non-invasive devices, especially on the accuracy, and sensor characteristics (size and thickness).
Conclusion: The BG seems related to the HR according to the results of this study: the more the BG, the higher the HR. Still more investigations need to be done on these conclusions, especially first by knowing the profile of the persons with which the data are processed, so that some external parameters that could have interfered and influenced the HR can be excluded, enabling the observations of the conclusions established previously to be fully interpreted and conclusive.
Furthermore, the market analysis highlighted several possible improvements on the diabetes devices market, especially the non-invasive one that is still new and full of opportunities.

Background: Nowadays, the tools and methods to measure Blood Glucose Levels (BGLs) are quite, invasive, and inconvenient, especially for diabetes patients. For these reasons, several studies are currently focusing on predicting and measuring the glycaemia using instruments such as near infrared spectroscopy (NIR) and Raman spectroscopy. Recently, a few studies tried to use the electrocardiogram (ECG) and its multiple features to establish a link between them and the blood glucose levels in order to predict the glycaemia without the need to prick the skin. The techniques used rely mostly on the extraction of a few segments and intervals from the ECG signals of patients, that have in parallel, their BGLs monitored continuously to establish a linear regression between some of these ECG parameters and the glycaemia.
Aims: This research relies on data (ECG and BGLs) obtained from Singular Wings Medical (SWM) company. The purpose is to use the multiresolution wavelet approach to extract heart rate (HR) parameters-related information of the ECG signals and link them to BGLs to analyse in which aspects the Blood Glucose (BG) and ECG are linked together. Research around the ECG and its link with BG could have an impact on the thought and design of a potential device that could measure glycemia from one or more of its features.
Material and methods: Two studies focused on following and analysing the HR and BG variations per days for several candidates, for which the information have been provided by SWM. The BG data, measured by the Freestyle Libre 2, were sampled at 1 data every 15 min and were directly accessible with excel files provided by SWM. The ECG sampled at 250 Hz and as 10 seconds signals were provided as srj files and processed under MATLAB software. The HR information was retrieved from 2 methods: using Wavelet transform (WT) with MATLAB software and using SWM calculations as a comparison and validation of the HR obtained from the WT.
The third study focused on correlation tests, with Thonny software (Python), between statistical parameters (mean, median, mode, standard deviation and variance) of BG and HR calculated previously with MATLAB.
To go further, the possibility of putting an ECG-BG device on the french market has been analysed through different tools, essentially to collect diabetic population needs and bring out recommendations on how to enter the market. This has been done through a questionnaire, a benchmarking and a swot analysis.
Results: Results from two first study showed a trend of the HR variations following the ones from BG. That’s to say when the BG increases, the HR tends to rise too and vice-versa. Correlation tests also showed that the median BG and mean HR, median BG and mode HR and mode BG and mode HR have positive linear relationship between each other, with respective correlation coefficients of 0,75998, 0,72 and 0,7122.
The market analysis highlighted the major key players (Abbott, Medtronic and Dexcom) in the Blood Glucose Monitoring (BGM) devices market as well as the strong points making their devices widely used among others. Also, the questionnaire has been filled by 155 diabetic persons that have high hopes for improvements of future non-invasive devices, especially on the accuracy, and sensor characteristics (size and thickness).
Conclusion: The BG seems related to the HR according to the results of this study: the more the BG, the higher the HR. Still more investigations need to be done on these conclusions, especially first by knowing the profile of the persons with which the data are processed, so that some external parameters that could have interfered and influenced the HR can be excluded, enabling the observations of the conclusions established previously to be fully interpreted and conclusive.
Furthermore, the market analysis highlighted several possible improvements on the diabetes devices market, especially the non-invasive one that is still new and full of opportunities.

TABLE OF CONTENTS
AKNOWLEDGMENT i
ABSTRACT ii
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS ix
Chapter 1: Introduction 1
1.1 Blood glucose or glycemia 1
1.1.1 Definition 1
1.1.2 Human regulation of glycemia 1
1.1.3 Diabetes 3
1.1.4 Monitoring of BGLs and current challenges 3
1.2 Electrocardiogram (ECG) 8
1.2.1 Structure and mechanism of heart function 8
1.2.2 History of the ECG 9
1.2.3 Record of the ECG 10
1.2.4 Reading of an ECG 11
1.2.5 Fast-growing portable and connected devices 12
1.3 Link between the ECG and glycemia (literature review) 13
1.4 Thesis research 15
Chapter 2: Materials and Method 16
2.1 Data preparation 16
2.1.1 ECGs 16
2.1.2 CGM data 17
2.1.3 Selection of candidates 18
2.1.4 Selection of recorded days 19
2.2 First study: average heart values vs blood glucose variations per time slot of the day 20
2.3 Second Study: HR and BG variations per day 22
2.4 Correlation tests between the HR and BG data per day 24
Chapter 3: Results 26
3.1 First study: average heart values vs blood glucose variations per time slot of the day 26
3.2 Second Study: HR and BG variations per day 28
3.3 Correlation tests between the HR and BG data per day 37
3.3.1 BG distribution analysis 37
3.3.2 HR distribution analysis 40
3.3.3 Correlation tests between HR statistical parameters and BG statistical parameters 43
Chapter 4: Discussion 46
4.1 First study: average heart values vs blood glucose variations per time slot of the day 46
4.2 Second Study: HR and BG variations per day 47
4.3 Correlation tests between the HR and BG data per day 48
4.3.1 BG distribution analysis 48
4.3.2 HR distribution analysis 49
4.3.3 Correlation tests between HR statistical parameters and BG statistical parameters 49
4.4 Interpretation of the results and their implications in a broader context 50
Chapter 5: Market Implementation 53
5.1 The french BGM devices market (size, drivers and key players) 53
5.2 Benchmarking 56
5.3 Questionnaire 66
5.3.1 Profile of respondents 66
5.3.2 Treatments and monitoring of glycemia 69
5.3.3 Focus on the glucometer device 71
5.3.4 Focus on the FGM and CGM devices 72
5.3.5 Motivation of respondents to monitor their glycemia and expectations for future devices 76
5.4 SWOT analysis 78
Chapter 6: Conclusion and perspective 80
REFERENCES 83
APPENDICES 87


LIST OF TABLES

Table 1: Categories of BG monitoring devices 4
Table 2: CGM data from SWM 17
Table 3: Selection of days by matching the ECGs and BG dates 20
Table 4: Days kept to conduct the analysis 20
Table 5: Shapiro-tests of BG and HR statistical parameters 24
Table 6: Results of the correlation tests 45
Table 7: Segmentation of the french BGM devices market 54
Table 8: Benchmarking comparing different systems of Blood Glucose Monitoring devices (information retrieved from the official compagnies websites) 58


LIST OF FIGURES


Figure 1: Glycemia homeostasis: when BG > 100 mg/dL, the pancreas will release the insulin hormone to store the glucose inside the muscles, liver and adipose tissue. On the contrary, when BG<70 mg/dL, the pancreas will release the glucagon hormone to help release the glucose stored in the previous organs in the bloodstream. (figure created with canva) 2
Figure 2: Cardiovascular system: the deoxygenated blood of the body enters in the right atrium through the vena cava and is expulsed toward the lungs by the ventricles and pulmonary artery to be oxygenated again. Once it’s the case, the oxygenated blood enters again in the heart, in the left atrium through the pulmonary veins, in ventricles and is expulsed by the aorta to irrigate the entire body. The four valves prevent the blood to flow back between the heart chambers. (figure created with biorender) 8
Figure 3: 12-lead ECG configuration usually done in hospitals, showing where the ECG sensors need to be placed on the body that is lying down, to record the electrical activity of the heart and obtain the ECG signal (figure created with canva) 10
Figure 4: Representation of an ECG and its features: one heartbeat is represented by this pattern: P-wave, QRS complex and T wave with different segments and intervals between them. Successive heartbeats constitute the ECG signal (figure created with biorender) 11
Figure 5: Extraction of tt and rows.ecgs fields to plot ECGs 17
Figure 6: Ambulatory glucose profiles of candidates 18
Figure 7: Methods to compare the average HR and BG per time slots and days selected 21
Figure 8: Method used to study HR from WT and glycemia variations 22
Figure 9: Types of ECG signals excluded 23
Figure 10: Method followed to study SWM estimated HR and glycemia variations 23
Figure 11: Comparison of the average BG and HR values per BG variations time slot (09/11) 26
Figure 12: Comparison of the average BG and HR values per BG variations time slot (10/12) 27
Figure 13: Comparison of the average BG and HR values per BG variations time slot (14/12) 28
Figure 14: HR (WT) and BG variations on 09/11, 10/12 and 14/12 for candidate 1735 29
Figure 15: HR (SWM) and BG variations on 09/11, 10/12 and 14/12 for candidate 1735 30
Figure 16: HR (WT and SWM) and BG variations on 08/10 for candidate 1698 32
Figure 17: HR (WT and SWM) and BG variations on 22/10 for candidate 1727 33
Figure 18: HR (WT) and BG variations on 10/11 and 14/11 for candidate 1741 34
Figure 19: HR (SWM) and BG variations on 10/11 and 14/11 for candidate 1741 35
Figure 20: HR (WT and SWM) and BG variations on 16/10 for candidate 114 36
Figure 21: BG distribution among the candidates 37
Figure 22: BG distribution among the candidates (part 2) 38
Figure 23: BG distribution model for candidate 1735 (09/11), 1727 and 1741 (14/11) 39
Figure 24: HR distribution among the candidates 40
Figure 25:HR distribution among the candidates (part 2) 41
Figure 26: HR distribution model for candidate 1735 (09/11), 1727 and 1741 (14/11) 42
Figure 27: Correlation tests between HR and BG statistical parameters 44
Figure 28: Profile of respondents 66
Figure 29: Age repartition among men and women respondents 67
Figure 30: Diabetes type distribution among men and women 67
Figure 31: Average age of diagnosis per diabetes type 68
Figure 32: Treatments taken per diabetes type 69
Figure 33: BGM device used per diabetes type 70
Figure 34: Glucometers: reasons of use and satisfaction levels according to respondents 71
Figure 35: Disadvantages of the glucometers 72
Figure 36: CGM and FGM devices used by respondents 73
Figure 37: CGM and FGM: reasons of use according to respondents 73
Figure 38: Satisfaction levels among CGM and FGM devices users 74
Figure 39: Disadvantages of the CGM and FGM devices 74
Figure 40: Motivation of respondents (monitoring already or not their glycemia) to monitor their BG with a new non-invasive device 76
Figure 41: Features expected by respondents for future non-invasive BGM devices 77
Figure 42: SWOT analysis for a possible implementation of an ECG-BG device 79

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