113 research outputs found

    Heart Rate Estimation During Physical Exercise Using Wrist-Type Ppg Sensors

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    Accurate heart rate monitoring during intense physical exercise is a challenging problem due to the high levels of motion artifacts (MA) in photoplethysmography (PPG) sensors. PPG is a non-invasive optical sensor that is being used in wearable devices to measure blood flow changes using the property of light reflection and absorption, allowing the extraction of vital signals such as the heart rate (HR). However, the sensor is susceptible to MA which increases during physical activity. This occurs since the frequency range of movement and HR overlaps, difficulting correct HR estimation. For this reason, MA removal has remained an active topic under research. Several approaches have been developed in the recent past and among these, a Kalman filter (KF) based approach showed promising results for an accurate estimation and tracking using PPG sensors. However, this previous tracker was demonstrated for a particular dataset, with manually tuned parameters. Moreover, such trackers do not account for the correct method for fusing data. Such a custom approach might not perform accurately in practical scenarios, where the amount of MA and the heart rate variability (HRV) depend on numerous, unpredictable factors. Thus, an approach to automatically tune the KF based on the Expectation-Maximization (EM) algorithm, with a measurement fusion approach is developed. The applicability of such a method is demonstrated using an open-source PPG database, as well as a developed synthetic generation tool that models PPG and accelerometer (ACC) signals during predetermined physical activities

    Harmonic Sum-based Method for Heart Rate Estimation using PPG Signals Affected with Motion Artifacts

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    Wearable photoplethysmography has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the HSUM model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint HSUM model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset

    The 2023 wearable photoplethysmography roadmap

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    Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

    Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

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    Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

    Description of a Database Containing Wrist PPG Signals Recorded during Physical Exercise with Both Accelerometer and Gyroscope Measures of Motion

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    Wearable heart rate sensors such as those found in smartwatches are commonly based upon Photoplethysmography (PPG) which shines a light into the wrist and measures the amount of light reflected back. This method works well for stationary subjects, but in exercise situations, PPG signals are heavily corrupted by motion artifacts. The presence of these artifacts necessitates the creation of signal processing algorithms for removing the motion interference and allowing the true heart related information to be extracted from the PPG trace during exercise. Here, we describe a new publicly available database of PPG signals collected during exercise for the creation and validation of signal processing algorithms extracting heart rate and heart rate variability from PPG signals. PPG signals from the wrist are recorded together with chest electrocardiography (ECG) to allow a reference/comparison heart rate to be found, and the temporal alignment between the two signal sets is estimated from the signal timestamps. The new database differs from previously available public databases because it includes wrist PPG recorded during walking, running, easy bike riding and hard bike riding. It also provides estimates of the wrist movement recorded using a 3-axis low-noise accelerometer, a 3-axis wide-range accelerometer, and a 3-axis gyroscope. The inclusion of gyroscopic information allows, for the first time, separation of acceleration due to gravity and acceleration due to true motion of the sensor. The hypothesis is that the improved motion information provided could assist in the development of algorithms with better PPG motion artifact removal performance

    Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

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    Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

    Pulse transit time measured by photoplethysmography improves the accuracy of heart rate as a surrogate measure of cardiac output, stroke volume and oxygen uptake in response to graded exercise

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    Heart rate (HR) is a valuable and widespread measure for physical training programs, although its description of conditioning is limited to the cardiac response to exercise. More comprehensive measures of exercise adaptation include cardiac output ((Q) over dot), stroke volume (SV) and oxygen uptake ((V) over dotO(2)), but these physiological parameters can be measured only with cumbersome equipment installed in clinical settings. In this work, we explore the ability of pulse transit time (PTT) to represent a valuable pairing with HR for indirectly estimating (Q) over dot, SV and (V) over dotO(2) non-invasively. PTT was measured as the time interval between the peak of the electrocardiographic (ECG) R-wave and the onset of the photoplethysmography (PPG) waveform at the periphery (i.e. fingertip) with a portable sensor. Fifteen healthy young subjects underwent a graded incremental cycling protocol after which HR and PTT were correlated with (Q) over dot, SV and (V) over dotO(2) using linear mixed models. The addition of PTT significantly improved the modeling of (Q) over dot, SV and (V) over dotO(2) at the individual level (R-1(2) = 0.419 for SV, 0.548 for (Q) over dot, and 0.771 for (V) over dotO(2)) compared to predictive models based solely on HR (R-1(2) = 0.379 for SV, 0.503 for (Q) over dot, and 0.745 for (V) over dotO(2)). While challenges in sensitivity and artifact rejection exist, combining PTT with HR holds potential for development of novel wearable sensors that provide exercise assessment largely superior to HR monitors

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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

    How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way

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    Despite the population of the noninvasive, economic, comfortable, and easy-to-install photoplethysmography (PPG), it is still lacking a mathematically rigorous and stable algorithm which is able to simultaneously extract from a single-channel PPG signal the instantaneous heart rate (IHR) and the instantaneous respiratory rate (IRR). In this paper, a novel algorithm called deppG is provided to tackle this challenge. deppG is composed of two theoretically solid nonlinear-type time-frequency analyses techniques, the de-shape short time Fourier transform and the synchrosqueezing transform, which allows us to extract the instantaneous physiological information from the PPG signal in a reliable way. To test its performance, in addition to validating the algorithm by a simulated signal and discussing the meaning of โ€œinstantaneous,โ€ the algorithm is applied to two publicly available batch databases, the Capnobase and the ICASSP 2015 signal processing cup. The former contains PPG signals relative to spontaneous or controlled breathing in static patients, and the latter is made up of PPG signals collected from subjects doing intense physical activities. The accuracies of the estimated IHR and IRR are compared with the ones obtained by other methods, and represent the state-of-the-art in this field of research. The results suggest the potential of deppG to extract instantaneous physiological information from a signal acquired from widely available wearable devices, even when a subject carries out intense physical activities
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