153 research outputs found

    An optimization study of estimating blood pressure models based on pulse arrival time for continuous monitoring

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    Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker correlation between the estimated BP and the cuff BP than MK-EE, MK-BH, and dMK-BH models. Further, in contrast to L-MK, the dMK-BH model had the strongest correlation and the smallest difference between the estimated BP and the cuff BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) than others. In this study, the simple MK-EE model showed the best similarity to the dMK-BH model. There were no significant changes between MK-EE and dMK-BH models. These findings indicated that the nonlinear MK-EE model with low estimated error and simple mathematical expression was a good choice for application in wearable sensor devices for cuff-less BP monitoring compared to others

    Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study

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    Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT). Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ยฑ 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations

    ์ปคํ”„๋ฆฌ์Šค ๋ฐฉ์‹์˜ ์ฐฉ์šฉํ˜• ์—ฐ์† ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2019. 2. ๊น€ํฌ์ฐฌ.๊ณ ํ˜ˆ์••์˜ ์กฐ๊ธฐ ์ง„๋‹จ๊ณผ ๊ณ ํ˜ˆ์•• ํ™˜์ž์˜ ํ˜ˆ์•• ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ผ์ƒ์ƒํ™œ์—์„œ์˜ ์ง€์†์ ์ธ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์ด ์ค‘์š”ํ•˜๋‹ค. ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ (Pulse transit time, PTT) ๊ธฐ๋ฐ˜์˜ ํ˜ˆ์•• ์ถ”์ • ๋ฐฉ์‹์ด ์ด๋ฅผ ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ€์žฅ ๊ฐ๊ด‘ ๋ฐ›๊ณ  ์žˆ์ง€๋งŒ, ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์—ฌ๋Ÿฌ ์ธก์ • ์žฅ์น˜๋“ค์ด ํ•„์š”ํ•˜์—ฌ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ์‚ฌ์šฉ์— ์ œ์•ฝ์ด ์žˆ์œผ๋ฉฐ, ๋˜ํ•œ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ๋งŒ์„ ์ด์šฉํ•œ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••(Systolic blood pressure, SBP) ์ถ”์ • ๋Šฅ๋ ฅ์€ ๋ถ€์กฑํ•จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์ฒซ ๋ฒˆ์งธ ๋ชฉ์ ์€ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ์ธก์ • ์‹œ์Šคํ…œ์„ ์ฐฉ์šฉํ˜•์œผ๋กœ ๊ฐœ๋ฐœํ•˜์—ฌ ๊ฐ„ํŽธํ•˜๊ฒŒ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„์„ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•จ์œผ๋กœ์จ ์ผ์ƒ ์ƒํ™œ ์ค‘ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„์„ ์ด์šฉํ•œ ์—ฐ์†์ ์ธ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ด‘์šฉ์ ๋งฅํŒŒ (Photoplethysmogram, PPG) ์™€ ์‹ฌ์ง„๋„ (Seismocardiogram, SCG)๋ฅผ ๋™์‹œ์— ์ธก์ •ํ•˜๋Š” ๊ฐ€์Šด ์ฐฉ์šฉํ˜• ๋‹จ์ผ ์žฅ์น˜๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ, ์‹ฌ์ง„๋„๋กœ๋ถ€ํ„ฐ ๋Œ€๋™๋งฅ ํŒ๋ง‰์˜ ์—ด๋ฆฌ๋Š” ์‹œ์ ์„, ๊ด‘์šฉ์ ๋งฅํŒŒ๋กœ๋ถ€ํ„ฐ ๋งฅํŒŒ์˜ ๋„์ฐฉ ์‹œ์ ์„ ํŠน์ •ํ•˜์—ฌ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์‹œ์Šคํ…œ์€ ๋‚ฎ์€ ์ „๋ ฅ ์†Œ๋ชจ์™€ ์†Œํ˜•์˜ ๊ฐ„ํŽธํ•œ ๋””์ž์ธ์„ ํ†ตํ•ด 24์‹œ๊ฐ„ ๋™์•ˆ ์—ฐ์†์ ์œผ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ธก์ •๋œ ์ƒ์ฒด์‹ ํ˜ธ๋กœ๋ถ€ํ„ฐ ์ถ”์ถœ๋œ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ๋ฐ ๊ธฐํƒ€ ํ˜ˆ์•• ๊ด€๋ จ ๋ณ€์ˆ˜๋“ค์ด ๊ธฐ๊ธฐ์˜ ๋ฐ˜๋ณต ์ฐฉ์šฉ์—๋„ ๋ณ€ํ•˜์ง€ ์•Š์Œ์„ ๊ธ‰๊ฐ„๋‚ด์ƒ๊ด€๊ณ„์ˆ˜(Intra-class correlation, ICC) ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๊ณ  (ICC >0.8), ๋˜ํ•œ ๋ณธ ์‹œ์Šคํ…œ์—์„œ ์‚ฌ์šฉ๋œ ์‹ฌ์ง„๋„๊ฐ€ ๋Œ€๋™๋งฅ ํŒ๋ง‰์˜ ์—ด๋ฆฌ๋Š” ์‹œ์ ์˜ ๋ ˆํผ๋Ÿฐ์Šค๊ฐ€ ๋  ์ˆ˜ ์žˆ๋Š”์ง€๋„ ์‹ฌ์ €ํ•ญ์‹ ํ˜ธ(Impedancecardiogram, ICG)์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค(r=0.79ยฑ0.14). ๋‘˜์งธ๋กœ, ๊ฐœ๋ฐœ๋œ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์กด์˜ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„๋งŒ์„ ์ด์šฉํ•œ ํ˜ˆ์•• ์ถ”์ • ๋ฐฉ์‹์„ ๋ณด์™„ํ•˜์—ฌ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์˜ ์ถ”์ • ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ์‹ฌ์ง„๋„์˜ ์ง„ํญ๊ณผ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„์„ ๊ฐ™์ด ์‚ฌ์šฉํ•˜๋Š” ๋‹ค๋ณ€์ˆ˜ ๋ชจ๋ธ์„ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• ์ถ”์ •์„ ์œ„ํ•ด ์ œ์•ˆํ•˜์˜€๊ณ , ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์œ ๋„๋œ ํ˜ˆ์•• ๋ณ€ํ™” ์ƒํ™ฉ์—์„œ, ๊ธฐ์กด์˜ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ํ˜น์€ ๋งฅํŒŒ๋„๋‹ฌ์‹œ๊ฐ„ (Pulse arrival time, PAT) ๋งŒ์„ ์ด์šฉํ•œ ๋ชจ๋ธ๊ณผ ๊ทธ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ ๋ชจ๋ธ์ด ๊ฐ„๋‹จํ•œ ๊ต์ •์ ˆ์ฐจ๋ฅผ ํ†ตํ•ด ์—ฌ๋Ÿฌ ์‚ฌ๋žŒ์—๊ฒŒ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์‚ดํŽด๋ณด์•˜๊ณ  ๋” ๋‚˜์•„๊ฐ€ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ์‚ฌ์šฉ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด์„œ๋„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋กœ ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ (1) ๊ธฐ์กด์˜ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ํ˜น์€ ๋งฅํŒŒ๋„๋‹ฌ์‹œ๊ฐ„ ๋งŒ์„ ์ด์šฉํ•œ ๋ชจ๋ธ๋ณด๋‹ค ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• ์ถ”์ • ๋Šฅ๋ ฅ ์ธก๋ฉด์—์„œ ๋” ์šฐ์ˆ˜ํ•˜์˜€๊ณ , (๊ฐ๊ฐ์˜ ํ‰๊ท ์ ˆ๋Œ€์˜ค์ฐจ๋Š” 4.57, 6.01, 6,11 mmHg ์˜€๋‹ค.) (2) ๊ฐ„๋‹จํ•œ ๊ต์ •์ ˆ์ฐจ๋งŒ์„ ํ†ตํ•ด์„œ ์—ฌ๋Ÿฌ ์‚ฌ๋žŒ์—๊ฒŒ ์ ์šฉ ๋˜์—ˆ์„ ๋•Œ์˜ ์ถ”์ • ๋Šฅ๋ ฅ์ด ๊ตญ์ œ ๊ธฐ์ค€์— ๋ถ€ํ•ฉํ•˜์˜€์œผ๋ฉฐ, (3) ์ผ์ƒ ์ƒํ™œ์—์„œ๋„ ์‚ฌ์šฉ์ž์˜ ์•„๋ฌด๋Ÿฐ ๊ฐœ์ž…์ด๋‚˜ ์ œ์•ฝ ์—†์ด ์ง€์†์ ์ธ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ์ฐฉ์šฉํ˜• ์—ฐ์† ํ˜ˆ์•• ์ธก์ • ์‹œ์Šคํ…œ์€ ๊ฐ€์Šด์— ๋ถ€์ฐฉํ•˜๋Š” ๋‹จ์ผ ๊ธฐ๊ธฐ ํ˜•ํƒœ๋กœ ๊ทธ ์‚ฌ์šฉ์ด ๊ฐ„ํŽธํ•  ๋ฟ ์•„๋‹ˆ๋ผ ์ผ์ƒ์ƒํ™œ ์ค‘์—์„œ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„๊ณผ ์‹ฌ์ง„๋„์˜ ์ง„ํญ์„ ์ด์šฉํ•˜์—ฌ ํ–ฅ์ƒ๋œ ์ˆ˜์ค€์˜ ์—ฐ์† ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•˜์˜€๋Š”๋ฐ”, ์ด๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ฐ”์ผ ํ—ฌ์Šค์ผ€์–ด ์„œ๋น„์Šค์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค.Continuous blood pressure (BP) monitoring is needed in daily life to enable early detection of hypertension and improve control of BP for hypertensive patients. Although the pulse transit time (PTT)-based BP estimation represents one of most promising approaches, its use in daily life is limited owing to the requirement of multi systems to measure PTT, and its performance in systolic blood pressure (SBP) estimation is not yet satisfactory. The first goal of this study is to develop a wearable system providing convenient measurement of the PTT, which facilitates continuous BP monitoring based on PTT in daily life. A single chest-worn device was developed measuring a photoplethysmogram (PPG) and a seismocardiogram (SCG) simultaneously, thereby obtaining PTT by using the SCG as timing reference of the aortic valve opening and the PPG as timing reference of pulse arrival. The presented device was designed to be compact and convenient to use, and to last for 24h by reducing power consumption of the system. The consistency of BP related parameters extracted from the system including PTT between repetitive measurements was verified by an intra-class correlation analysis, and it was over 0.8 for all parameters. In addition, the use of SCG as timing reference of the aortic valve opening was verified by comparing it with an impedance cardiogram (r = 0.79 ยฑ 0.14). Secondly, the algorithm improving the performance of the SBP estimation was developed by using the presented system. A multivariate model using SCG amplitude (SA) in conjunction with PTT was proposed for SBP estimation, and was compared with conventional models using only PTT or pulse arrival time (PAT) in various interventions inducing BP changes. Furthermore, we validated the proposed model against the general population with a simple calibration process and verified its potential for daily use. The results suggested that (1) the proposed model, which employed SA in conjunction with PTT for SBP estimation, outperformed the conventional univariate model using PTT or PAT (the mean absolute errors were of 4.57, 6.01, and 6.11 for the proposed, PTT, and PAT models, respectively)(2) for practical use, the proposed model showed potential to be generalized with a simple calibrationand (3) the proposed model and system demonstrated the potential for continuous BP monitoring in daily life without any intervention of users or regulations. In conclusion, the presented system provides an improved performance of continuous BP monitoring in daily life by using a combination of PTT and SA with a convenient and compact single chest-worn device, and thus, it can contribute to mobile healthcare services.CONTENTS Abstract i Contents v List of Tables ix List of Figures xi List of Abbreviations xvi Chapter 1 1 General Introduction 1.1. Blood pressure 2 1.2. Pulse transit time 6 1.3. Thesis objective 12 Chapter 2 14 Development of the Wearable Blood Pressure Monitoring System 2.1. Introduction 15 2.2. System overview 17 2.3. Bio-signal instrumentation 21 2.4. Power management 24 2.5. PCB and case design 25 2.6. Software Design 27 2.7. Signal Processing 30 2.8. Experimental setup 34 2.8.1. Repeatability test 34 2.8.2. Verification of SCG-based PEP 35 2.9. Results and Discussion 38 2.9.1. Repeatability test 38 2.9.2. Verification of SCG-based PEP 40 Chapter 3 43 Enhancement of PTT based BP estimation 3.1. Introduction 44 3.2. Method 47 3.2.1. Principle of BP estimation 47 3.2.2. Subjects 49 3.2.3. Study protocol 50 3.2.4. Data collection 56 3.2.5. Data analysis 60 3.2.6. Evaluation standard 64 3.3. Results 67 3.4. Discussion 96 Chapter 4 113 Conclusion 4.1. Thesis Summary and Contributions 114 4.2. Future Direction 116 Bibliography 118 Abstract in Korean 128Docto

    Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

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    Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010-2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology

    A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation

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    Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial Blood Pressure (ABP) signals as dependent variables, and then using machine learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR), and Heart Rate (HR) features from Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster. The method was implemented using the MIMICII dataset with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multi-trend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster, and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36)

    ๋Œ€๊ทœ๋ชจ ์ธ๊ตฌ ๋ชจ๋ธ๊ณผ ๋‹จ์ผ ๊ฐ€์Šด ์ฐฉ์šฉํ˜• ์žฅ์น˜๋ฅผ ํ™œ์šฉํ•œ ๋น„์นจ์Šต์  ์—ฐ์† ๋™๋งฅ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2021. 2. ๊น€ํฌ์ฐฌ.์ตœ๊ทผ ์ˆ˜์‹ญ ๋…„ ๋™์•ˆ ๋น„์นจ์Šต์  ์—ฐ์† ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์— ๋Œ€ํ•œ ํ•„์š”์„ฑ์ด ์ ์ฐจ ๋Œ€๋‘๋˜๋ฉด์„œ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„, ๋งฅํŒŒ ๋„๋‹ฌ ์‹œ๊ฐ„, ๋˜๋Š” ๊ด‘์šฉ์ ๋งฅํŒŒ์˜ ํŒŒํ˜•์œผ๋กœ๋ถ€ํ„ฐ ์ถ”์ถœ๋œ ๋‹ค์–‘ํ•œ ํŠน์ง•๋“ค์„ ์ด์šฉํ•œ ํ˜ˆ์•• ์ถ”์ • ์—ฐ๊ตฌ๋“ค์ด ์ „์„ธ๊ณ„์ ์œผ๋กœ ํ™œ๋ฐœํ•˜๊ฒŒ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋“ค์€ ๊ตญ์ œ ํ˜ˆ์•• ํ‘œ์ค€์„ ๋งŒ์กฑ์‹œํ‚ค์ง€ ๋ชปํ•˜๋Š” ๋งค์šฐ ์ ์€ ์ˆ˜์˜ ํ”ผํ—˜์ž๋“ค ๋งŒ์„ ๋Œ€์ƒ์œผ๋กœ ์ฃผ๋กœ ํ˜ˆ์•• ์ถ”์ • ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ ๋ฐ ๊ฒ€์ฆํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์„ฑ๋Šฅ์˜ ์ •ํ™•๋„๊ฐ€ ์ ์ ˆํ•˜๊ฒŒ ๊ฒ€์ฆ๋˜์ง€ ๋ชปํ–ˆ๋‹ค๋Š” ํ•œ๊ณ„์ ์ด ์žˆ์—ˆ๊ณ , ๋˜ํ•œ ํ˜ˆ์•• ์ถ”์ • ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ถœ์„ ์œ„ํ•œ ์ƒ์ฒด ์‹ ํ˜ธ๋“ค์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋Œ€๋ถ€๋ถ„ ๋‘ ๊ฐœ ์ด์ƒ์˜ ๋ชจ๋“ˆ์„ ํ•„์š”๋กœ ํ•˜๋ฉด์„œ ์‹ค์šฉ์„ฑ ์ธก๋ฉด์—์„œ ํ•œ๊ณ„์ ์ด ์žˆ์—ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ๋Œ€๊ทœ๋ชจ ์ƒ์ฒด์‹ ํ˜ธ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋“ค์„ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์ž„์ƒ์ ์œผ๋กœ ํ—ˆ์šฉ ๊ฐ€๋Šฅํ•œ ์ˆ˜์ค€์˜ ์ •ํ™•๋„๊ฐ€ ์ ์ ˆํžˆ ๊ฒ€์ฆ๋œ ํ˜ˆ์•• ์ถ”์ • ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 1376๋ช…์˜ ์ˆ˜์ˆ  ์ค‘ ํ™˜์ž๋“ค์˜ ์•ฝ 250๋งŒ ์‹ฌ๋ฐ• ์ฃผ๊ธฐ์— ๋Œ€ํ•ด ์ธก์ •๋œ ๋‘ ๊ฐ€์ง€ ๋น„์นจ์Šต์  ์ƒ์ฒด์‹ ํ˜ธ์ธ ์‹ฌ์ „๋„์™€ ๊ด‘์šฉ์ ๋งฅํŒŒ๋ฅผ ํ™œ์šฉํ•œ ํ˜ˆ์•• ์ถ”์ • ๋ฐฉ์‹๋“ค์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋งฅํŒŒ ๋„๋‹ฌ ์‹œ๊ฐ„, ์‹ฌ๋ฐ•์ˆ˜, ๊ทธ๋ฆฌ๊ณ  ๋‹ค์–‘ํ•œ ๊ด‘์šฉ์ ๋งฅํŒŒ ํŒŒํ˜• ํ”ผ์ฒ˜๋“ค์„ ํฌํ•จํ•˜๋Š” ์ด 42 ์ข…๋ฅ˜์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ”ผ์ฒ˜ ์„ ํƒ ๊ธฐ๋ฒ•๋“ค์„ ์ ์šฉํ•œ ๊ฒฐ๊ณผ, 28๊ฐœ์˜ ํ”ผ์ฒ˜๋“ค์ด ํ˜ˆ์•• ์ถ”์ • ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ ๊ฒฐ์ •๋˜์—ˆ๊ณ , ํŠนํžˆ ๋‘ ๊ฐ€์ง€ ๊ด‘์šฉ์ ๋งฅํŒŒ ํ”ผ์ฒ˜๋“ค์ด ๊ธฐ์กด์— ํ˜ˆ์•• ์ถ”์ • ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ ๊ฐ€์žฅ ์ฃผ์š”ํ•˜๊ฒŒ ํ™œ์šฉ๋˜์—ˆ๋˜ ๋งฅํŒŒ ๋„๋‹ฌ ์‹œ๊ฐ„๋ณด๋‹ค ์šฐ์›”ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์„ ์ •๋œ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ํ˜ˆ์••์˜ ๋‚ฎ์€ ์ฃผํŒŒ์ˆ˜ ์„ฑ๋ถ„์„ ์ธ๊ณต์‹ ๊ฒฝ๋ง์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ๋†’์€ ์ฃผํŒŒ์ˆ˜ ์„ฑ๋ถ„์„ ์ˆœํ™˜์‹ ๊ฒฝ๋ง์œผ๋กœ ๋ชจ๋ธ๋ง ํ•œ ๊ฒฐ๊ณผ, ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• ์—๋Ÿฌ์œจ 0.05 ยฑ 6.92 mmHg์™€ ์ด์™„๊ธฐ ํ˜ˆ์•• ์—๋Ÿฌ์œจ -0.05 ยฑ 3.99 mmHg ์ •๋„์˜ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค. ๋˜ ๋‹ค๋ฅธ ์ƒ์ฒด์‹ ํ˜ธ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์—์„œ ์ถ”์ถœํ•œ 334๋ช…์˜ ์ค‘ํ™˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ๋ชจ๋ธ์„ ์™ธ๋ถ€ ๊ฒ€์ฆํ–ˆ์„ ๋•Œ ์œ ์‚ฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ํš๋“ํ•˜๋ฉด์„œ ์„ธ ๊ฐ€์ง€ ๋Œ€ํ‘œ์  ํ˜ˆ์•• ์ธก์ • ์žฅ๋น„ ๊ธฐ์ค€๋“ค์„ ๋ชจ๋‘ ๋งŒ์กฑ์‹œ์ผฐ๋‹ค. ํ•ด๋‹น ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ํ˜ˆ์•• ์ถ”์ • ๋ชจ๋ธ์ด 1000๋ช… ์ด์ƒ์˜ ๋‹ค์–‘ํ•œ ํ”ผํ—˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ ์šฉ ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ผ์ƒ ์ƒํ™œ ์ค‘ ์žฅ๊ธฐ๊ฐ„ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€๋Šฅํ•œ ๋‹จ์ผ ์ฐฉ์šฉํ˜• ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ๊ธฐ์กด ํ˜ˆ์•• ์ถ”์ • ์—ฐ๊ตฌ๋“ค์€ ํ˜ˆ์•• ์ถ”์ • ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ถœ์„ ์œ„ํ•ด ํ•„์š”ํ•œ ์ƒ์ฒด์‹ ํ˜ธ๋“ค์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ตฐ๋ฐ ์ด์ƒ์˜ ์‹ ์ฒด ์ง€์ ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ๋ชจ๋“ˆ์„ ๋ถ€์ฐฉํ•˜๋Š” ๋“ฑ ์‹ค์šฉ์„ฑ ์ธก๋ฉด์—์„œ ํ•œ๊ณ„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ฌ์ „๋„์™€ ๊ด‘์šฉ์ ๋งฅํŒŒ๋ฅผ ๋™์‹œ์— ์—ฐ์†์ ์œผ๋กœ ์ธก์ •ํ•˜๋Š” ๋‹จ์ผ ๊ฐ€์Šด ์ฐฉ์šฉํ˜• ๋””๋ฐ”์ด์Šค๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๊ณ , ๊ฐœ๋ฐœ๋œ ๋””๋ฐ”์ด์Šค๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ด 25๋ช…์˜ ๊ฑด๊ฐ•ํ•œ ํ”ผํ—˜์ž๋“ค๋กœ๋ถ€ํ„ฐ ๋ฐ์ดํ„ฐ๋ฅผ ํš๋“ํ•˜์˜€๋‹ค. ์†๊ฐ€๋ฝ์—์„œ ์ธก์ •๋œ ๊ด‘์šฉ์ ๋งฅํŒŒ์™€ ๊ฐ€์Šด์—์„œ ์ธก์ •๋œ ๊ด‘์šฉ์ ๋งฅํŒŒ ๊ฐ„ ํŒŒํ˜•์˜ ํŠน์„ฑ์— ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ€์Šด์—์„œ ์ธก์ •๋œ ๊ด‘์šฉ์ ๋งฅํŒŒ์—์„œ ์ถ”์ถœ๋œ ํ”ผ์ฒ˜๋“ค์„ ๋Œ€์‘๋˜๋Š” ์†๊ฐ€๋ฝ์—์„œ ์ธก์ •๋œ ๊ด‘์šฉ์ ๋งฅํŒŒ ํ”ผ์ฒ˜๋“ค๋กœ ํŠน์„ฑ์„ ๋ณ€ํ™˜ํ•˜๋Š” ์ „๋‹ฌ ํ•จ์ˆ˜ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. 25๋ช…์œผ๋กœ๋ถ€ํ„ฐ ํš๋“ํ•œ ๋ฐ์ดํ„ฐ์— ์ „๋‹ฌ ํ•จ์ˆ˜ ๋ชจ๋ธ์„ ์ ์šฉ์‹œํ‚จ ํ›„ ํ˜ˆ์•• ์ถ”์ • ๋ชจ๋ธ์„ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ, ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• ์—๋Ÿฌ์œจ 0.54 ยฑ 7.47 mmHg์™€ ์ด์™„๊ธฐ ํ˜ˆ์•• ์—๋Ÿฌ์œจ 0.29 ยฑ 4.33 mmHg๋กœ ๋‚˜ํƒ€๋‚˜๋ฉด์„œ ์„ธ ๊ฐ€์ง€ ํ˜ˆ์•• ์ธก์ • ์žฅ๋น„ ๊ธฐ์ค€๋“ค์„ ๋ชจ๋‘ ๋งŒ์กฑ์‹œ์ผฐ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž„์ƒ์ ์œผ๋กœ ํ—ˆ์šฉ ๊ฐ€๋Šฅํ•œ ์ˆ˜์ค€์˜ ์ •ํ™•๋„๋กœ ์žฅ๊ธฐ๊ฐ„ ์ผ์ƒ ์ƒํ™œ์ด ๊ฐ€๋Šฅํ•œ ๋น„์นจ์Šต์  ์—ฐ์† ๋™๋งฅ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ•˜๊ณ  ๋‹ค์ˆ˜์˜ ๋ฐ์ดํ„ฐ์…‹์„ ๋Œ€์ƒ์œผ๋กœ ๊ฒ€์ฆํ•จ์œผ๋กœ์จ ๊ณ ํ˜ˆ์•• ์กฐ๊ธฐ ์ง„๋‹จ ๋ฐ ์˜ˆ๋ฐฉ์„ ์œ„ํ•œ ๋ชจ๋ฐ”์ผ ํ—ฌ์Šค์ผ€์–ด ์„œ๋น„์Šค์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค.As non-invasive continuous blood pressure monitoring (NCBPM) has gained wide attraction in the recent decades, many studies on blood pressure (BP) estimation using pulse transit time (PTT), pulse arrival time (PAT), and characteristics extracted from the morphology of photoplethysmogram (PPG) waveform as indicators of BP have been conducted. However, most of the studies have used small homogeneous subject pools to generate models of BP, which led to inconsistent results in terms of accuracy. Furthermore, the previously proposed modalities to measure BP indicators are questionable in terms of practicality, and lack the potential for being utilized in daily life. The first goal of this thesis is to develop a BP estimation model with clinically valid accuracy using a large pool of heterogeneous subjects undergoing various surgeries. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and PPG, from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate, and various PPG morphology features. BP estimation models were constructed using linear regression, random forest, artificial neural network (ANN), and recurrent neural network (RNN), and the performances were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ยฑ 6.92 mmHg for systolic blood pressure (SBP), and -0.05 ยฑ 3.99 mmHg for diastolic blood pressure (DBP) were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three international standards concerning the accuracy of BP monitors. The results indicate that the proposed method can be applied to large number of subjects and various subject phenotypes. The second goal of this thesis is to develop a wearable BP monitoring system, which facilitates NCBPM in daily life. Most previous studies used two or more modules with bulky electrodes to measure biosignals such as ECG and PPG for extracting BP indicators. In this study, a single wireless chest-worn device measuring ECG and PPG simultaneously was developed. Biosignal data from 25 healthy subjects measured by the developed device were acquired, and the BP estimation model developed above was tested on this data after applying a transfer function mapping the chest PPG morphology features to the corresponding finger PPG morphology features. The model yielded mean errors of 0.54 ยฑ 7.47 mmHg for SBP, and 0.29 ยฑ 4.33 mmHg for DBP, again satisfying the three standards for the accuracy of BP monitors. The results indicate that the proposed system can be a stepping stone to the realization of mobile NCBPM in daily life. In conclusion, the clinical validity of the proposed system was checked in three different datasets, and it is a practical solution to NCBPM due to its non-occlusive form as a single wearable device.Abstract i Contents iv List of Tables vii List of Figures viii Chapter 1 General Introduction 1 1.1 Need for Non-invasive Continuous Blood Pressure Monitoring (NCBPM) 2 1.2 Previous Studies for NCBPM 5 1.3 Issues with Previous Studies 9 1.4 Thesis Objectives 12 Chapter 2 Non-invasive Continuous Arterial Blood Pressure Estimation Model in Large Population 14 2.1 Introduction 15 2.1.1 Electrocardiogram (ECG) and Photoplethysmogram (PPG) Features for Blood Pressure (BP) Estimation 15 2.1.2 Description of Surgical Biosignal Databases 16 2.2 Feature Analysis 19 2.2.1 Data Acquisition and Data Pre-processing 19 2.2.2 Feature Extraction 25 2.2.3 Feature Selection 35 2.3 Construction of the BP Estimation Models 44 2.3.1 Frequency Component Separation 44 2.3.2 Modelling Algorithms 47 2.3.3 Summary of Training and Validation 52 2.4 Results and Discussion 54 2.4.1 Feature Analysis 54 2.4.1.1 Pulse Arrival Time versus Pulse Transit Time 54 2.4.1.2 Feature Selection 57 2.4.2 Optimization of the BP Estimation Models 63 2.4.2.1 Frequency Component Separation 63 2.4.2.2 Modelling Algorithms 66 2.4.2.3 Comparison against Different Modelling Settings 68 2.4.3 Performance of the Best-case BP Estimation Model 69 2.4.4 Limitations 75 2.5 Conclusion 78 Chapter 3 Development of the Single Chest-worn Device for Non-invasive Continuous Arterial Blood Pressure Monitoring 80 3.1 Introduction 81 3.2 Development of the Single Chest-worn Device 84 3.2.1 Hardware Development 84 3.2.2 Software Development 90 3.2.3 Clinical Trial 92 3.3 Development of the Transfer Function 95 3.3.1 Finger PPG versus Chest PPG 95 3.3.2 The Concept of the Transfer Function 97 3.3.3 Data Acquisition for Modelling of the Transfer Function 98 3.4 Results and Discussion 100 3.4.1 Construction of the Transfer Function 100 3.4.2 Test of the BP Estimation Model 101 3.4.3 Comparison with the Previous Study using the Single Chest-worn Device 104 3.4.4 Limitations 106 3.5 Conclusion 108 Chapter 4 Thesis Summary and Future Direction 109 4.1 Summary and Contributions 110 4.2 Future Work 113 Bibliography 115 Abstract in Korean 129 Acknowledgement 132Docto
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