臺灣博碩士論文加值系統

心血管疾病為全球第一大死因，現今這已不再只是個健康問題，疾病的發生會造成大量的社會照護成本與經濟負擔，物聯網裝置的崛起帶來了全球化的系統整合，讓臨床健康量測、診斷、治療手段更加個人化、即時、方便並減少大量醫療成本。穿戴式物聯網裝置能量測生命徵象及身體活動量並協助使用者制訂健康計畫，維持身體活動、健康習慣與減少心血管疾病的發生率，然而現有裝置目前發展困境大多是量測功能不足以及無策略性的收集健康數據，以至於無法清楚且精準地透過大數據分析來預防疾病發生，因此本研究提出結合物聯網的軟硬體整合之穿戴式健康照護裝置進行全天候生命徵象量測，該量測包括動靜態心率、心房顫動、血壓趨勢、血氧飽和度、心率變異度、睡眠周期等長期分析，並且在臨床驗證物聯網裝置的確效性，同時透過人工智慧與機器學習等演算法輔助，提升量測模型的精準度。研究結果顯示透過本研究開發之穿戴式物聯網裝置，能夠收集足夠且可信度高的生理數據，並成功建立醫療專家與健身專家的人工智慧平台，得以同時各別針對專業人士與使用者提供有效的資訊，例如在醫療與健身終端提供即時回饋和長期健康趨勢評估，可望提升健康生活品質。

Cardiovascular disease (CVD) is the leading cause of the death all over the world, and this health issues also bring about abundant economic burdens. The global popular technology, Internet of Things (IoT), can integrate the hardware system of health monitoring, diagnostics and treatment, and making it more personalized, timely, and convenient in a lower cost. Wearable IoT may monitor vital signs and physical activities and promote a health program to maintain an active lifestyle, develop healthy habits for reducing the morbidity of CVD. However, the existing wearable devices still confront big challenges of insufficient function and poor strategy of big data acquisition for bio-data analysis. Therefore, this study proposed a wearable Hardware/Software (HW/SW) co-design wrist-type PPG device for IoT healthcare system, which incorporate with 24-hours vital sign AI-monitoring. To verify the clinical requirement, this study conducted a long-term clinical trial to validate different function including heart rate variability, blood pressure trend, atrial fibrillation, blood oxygen level, sleep cycle. Artificial intelligence and machine learning techniques are used to increase the measuring accuracy. The results shown that our lab-developed wrist-type PPG device was verified to acquire the sufficient and reliable bio-data. The AI-platform was also successfully established to provide specialists and users helpful information, such as timely noticing the abnormal vital signs or long-term healthy trend shown on the terminal device interface. By these means, we expecting the quality of healthy life would be raised up.

Table of Contents
Acknowledgments i
摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables x
Chapter 1. Introduction 1
Chapter 2. Materials and Methods 15
2.1 Architecture of the vital signs screening platform based on wrist-type PPG device 15
2.2 Hardware design of wrist-type device based on optical sensor 16
2.3 Noise reduction of PPG signal 22
2.3.1 The algorithm approach for noise reduction 24
2.3.2 The HW/SW approach for noise reduction 26
2.4 The approach of real time heart rate detection 26
2.4.1 Heart rate detection and the verification of PPI measured from PPG 27
2.4.2 Steady state measurement test for validation of the measured PPI from PPG 28
2.4.3 Running test on the treadmill for validation of dynamic heart rate 29
2.5 Atrial fibrillation detection algorithm based on wristband 30
2.6 Blood pressure prediction algorithm based on wristband 33
2.6.1 Data acquisition and clinical trial design 33
2.6.2 The architecture of the signal processing based on ML-model with feedback calibration method using PPG signal 35
2.6.3 Statistical Analysis based on international standards for the accuracy of the blood pressure estimation 40
2.7 Blood oxygen level algorithm with adaptive calibration based on wristband 40
2.8 Wrist-type heart rate variability analysis for cardiac telerehabilitation program 42
2.9 The application of the proposed wearable HW/SW co-design for IoT kernel-based system on medical and fitness expert AI-platform according to the personal baseline health index and all-day auto-monitoring 45
Chapter 3. Results 48
3.1 The correlation between PPI of PPG and RRI of ECG 48
3.2 Clinical validation for atrial fibrillation detection based on wrist-type PPG device 50
3.3 Clinical validation for blood pressure prediction based on wristband 54
3.3.1 Clinical validation for blood pressure prediction based on wristband 57
3.3.2 Breath-holding test for continuously vital sign monitor based on wrist-type PPG device 59
3.4 Clinical validation for blood oxygen level dropping detection based on wrist-type PPG device 61
3.5 Telemetry Analysis of Heart Rate Variability for Risk Evaluation of Coronary Artery Disease in Cardiac Telerehabilitation Program based on wristband 64
3.6 The application of the proposed wearable HW/SW co-design for IoT kernel-based system on medical and fitness expert AI-platform and overall software interface 66
Chapter 4. Discussions 69
4.1 Hardware and sensing interface analysis for medical and fitness expert AI-platform 69
4.2 Heart rate and dynamic characteristic under motion situation 69
4.3 Atrial fibrillation all-day auto-monitoring for earlier stroke prevention 70
4.4 The extension of blood pressure trend using wrist-type PPG device with personal calibration by FDA-approval sphygmomanometer 71
4.5 Blood oxygen level dropping detection for sleep apnea early screening 72
4.6 Heart rate variability analysis using wrist-type PPG device for risk evaluation of coronary artery disease in cardiac telerehabilitation Program 72
4.7 Extremely user experience for app design based on all-day auto-monitoring 73
4.8 The application of the specific field for medical and fitness expert purpose 73
Chapter 5. Conclusion 75
References 76




List of Figures
Figure 1. On World Heart Day World Health Organization (WHO) calls for accelerated action to prevent the world’s leading global killer – CVDs. 2
Figure 2. Most of the best and newest devices on the market with advanced physiological parameter are required using an index finger to press the metal electrode on the band surface. 4
Figure 3. The architecture of the AI-platform of wrist-type PPG device based on mobile app and cloud for assisting medical and fitness expert. 15
Figure 4. The proposed wrist-type PPG device for blood pressure estimation using reflective PPG. (A) The bottom of the proposed wrist-type PPG device integrated with PPG biosensor that should be contacted with the skin inseparably. (B) The practical diagram of wearing a device was for blood pressure estimating and transmitted physiological data wirelessly to mobile phone for blood pressure monitoring. (C) The 24-hour ABPM with other vital signs like average of heart rate (HR) and flip count. 17
Figure 5. The paradigm of the detailed circuit of proposed wrist-type PPG device. 19
Figure 6. The hardware system architecture of wrist-type PPG device. 19
Figure 7. The optical principle of PPG sensor that reflecting the blood volume variation from PPG signal. 20
Figure 8. The illustration of the corrupted PPG signal with noise interference (blue dash line) and recovery PPG signal with noise cancellation (red line). 22
Figure 9. The illustration of the PPG signal under exercise condition. The upper figure illustrates a short-term moving back and forth along the X-axis and the lower figure illustrates moving back and forth along the X-axis with steady 2-Hz. 23
Figure 10. Schematic diagram between LED and photodiode, red spot, partition plate; light green, PPG signal under skin; dark green, noise signal from the skin surface. 24
Figure 11. PPG signal under real time measurement. (A) PPG signal under stable condition (B) PPG signal under motion condition. 25
Figure 12. The proposed flow chart of the adaptive filter algorithm for motion artifact reduction. 26
Figure 13. The flow chart of digital signal processing of the heart rate detection. 27
Figure 14. The flow chart of PPI and power spectral density and RR interval (RRI) signals separately from PPG and ECG. 28
Figure 15. The running test design using the treadmill for validating the accuracy of wrist-type device with PPG sensor with the golden standard on the ECG chest strap. 29
Figure 16. The illustration of the validation experiment for running test on the treadmill. 30
Figure 17. The protocol of PPG measurement for AF detection algorithm on wrist-type PPG device. 31
Figure 18. The flowchart of RR-based machine learning model for AF detection and stroke prediction based on population cohort study. 32
Figure 19. The protocol of PPG estimation for proposed BP algorithm based on wrist-type PPG device. Phase 1 was for static state validation and Phase 2 was for dynamic state validation. 34
Figure 20. The characteristic of PPG morphology corresponds to the pulsatile PPG waveform. 36
Figure 21. The block diagram of the adaptive calibration blood pressure estimator with ML-based model for proposed wrist-type PPG device. Wrist-type PPG device: acquired PPG waveform continually for feature extraction. Sphygmomanometers: FDA-approval device for measurement BP values accurately as the reference for calibration use. PPG database: ML modeling based on PPG morphology characteristic parameter, personal information parameter and Real BP Label. State A: predict BP values with real information such as age and sex as input parameters before calibration. State B: predict BP values with optimized model including only PPG morphology characteristic parameter after calibration. 37
Figure 22. The protocol of PPG measurement for SpO2 dropped detection algorithm based on wrist-type PPG device. 42
Figure 23. Proposed flowchart of adaptive noise reduction for SpO2 42
Figure 24. The protocol of PPG measurement for HRV analysis based on wrist-type PPG device. 43
Figure 25. The scheme of all-day auto-monitoring and screening from the wrist-type PPG device. 46
Figure 26. The structure of personal health matrix for baseline construction. 46
Figure 27. Unsupervised learning based on similarity-based model with ML and DL method for personal baseline health index. 47
Figure 28. PPG and ECG waveform. 48
Figure 29. (a) PPI of PPG (b) RRI of ECG. 48
Figure 30. (A) the correlation coefficient of the PPI of PPG and the RRI of ECG (B) the scatter plot demonstrates the comparison of PPI and RRI using t-test. 49
Figure 31. The heart rate error results of the running test. 50
Figure 32. PPG waveform and correspond to the PPI of the normal sinus rhythm and AF. 51
Figure 33. Spectrum and spectrogram of the normal sinus rhythm, AF and normal sinus rhythm with low SNR. 52
Figure 34. 3D distribution by time domain features for training and testing. Upper figure shows the overall distribution with low SNR normal sinus rhythm. Lower figure shows the distribution without low SNR normal sinus rhythm. 53
Figure 35. The histogram of the age distribution from 15 to 95 years old from PPG database. (A) Age distribution for SBP. Blue column represented the number of low SBP below 100 mmHg. Orange column represented the number of normal SBP between 100 and 130 mmHg. Yellow column represented the number of high SBP above 130 mmHg. (B) Age distribution for DBP. Blue column represented the number of low DBP below 65 mmHg. Orange column represented the number of normal DBP between 65 and 85 mmHg. Yellow column represented the number of high DBP above 85 mmHg. 55
Figure 36. The Bland-Altman analysis of SBP and DBP for static state validation. Without calibration was on the top. Without calibration was on the bottom. (A) The scatter plot of real SBP and estimated SBP and Bland-Altman plot of the average of real SBP and estimated SBP and the difference between real SBP and estimated SBP. (B) The scatter plot of real DBP and estimated DBP and Bland-Altman plot of the average of real DBP and estimated DBP and the difference between real DBP and estimated DBP. 59
Figure 37. Dynamic state validation through breath-holding test with continuously vital sign monitor based on wrist-type PPG device for BP (blue dot was for SBP, purple dot was for DBP) and pulse oximeter for HR (red dot) and SpO2 (green dot). Two example test were showed that (a) was for test 1 and (b) was for test 2. 60
Figure 38. The destroyed PPG waveform of the red light and infrared radiation under motion scenario. 62
Figure 39. Individual breath holding results between the self-designed wearable device and FDA-approval wearable digital pulse oximeter. 63
Figure 40. Real-time video for SpO2 comparison between proposed self-designed wearable device, FDA-approval wearable digital pulse oximeter and cheaper fingertip pulse oximeter. 63
Figure 41. Software interface for HRV evaluation with health risk as the reference. 65
Figure 42. A novel reliable biomarker related to HRV based on wearable device benefited CAD patients in CR program. 65
Figure 43. User interface of software app for wrist-type PPG device. 67
Figure 44. Blood pressure measurement with personal calibration and real time vital sign measurement based on software app. 67
Figure 45. Daily report and vital sign trend with more detailed computation and abnormal notification on software app. 68
Figure 46. Specific fitness training result for general people by wrist-type PPG device. 68
Figure 47. The application of the home-based healthcare system from the proposed AI-platform. 74
Figure 48. The application of the gym-based AI fitness coach system from the proposed wearable AI-platform. 74

List of Tables
Table 1. Valuation of wearable devices and IoT in the global market. 3
Table 2. Various pulse oximeter approved by FDA. 12
Table 3. PPG sensor specifications. 21
Table 4. The detail about clinical trial database collection for BP estimation modeling. 33
Table 5. PPG morphology characteristic parameter and personal information parameter for BP prediction model. 36
Table 6. Clinical validation for AF based wrist-type PPG device. 54
Table 7. Accuracy comparison between all the ML models for SBP and DBP. 56
Table 8. The results of blood pressure estimation model without calibration and with calibration. 58
Table 9. Statistical results and confusion matrix for sleep apnea early detection. 62

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