6,122 research outputs found

    Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

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    Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time series-to-time series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.54 mmHg and a standard deviation of 23.7 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology

    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

    Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

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    Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

    ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ํ˜ˆ์•• ์˜ˆ์ธก ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์œค์„ฑ๋กœ.While COVID-19 is changing the world's social profile, it is expected that the telemedicine sector, which has not been activated due to low regulation and reliability, will also undergo a major change. As COVID-19 spreads in the United States, the US Department of Health \& Human Services temporarily loosens the standards for telemedicine, while enabling telemedicine using Facebook, Facebook Messenger-based video chat, Hangouts, and Skype. The expansion of the telemedicine market is expected to quickly transform the existing treatment-oriented hospital-led medical market into a digital healthcare service market focused on prevention and management through wearables, big data, and health records analysis. In this prevention and management-oriented digital healthcare service, it is very important to develop a technology that can easily monitor a person's health status. One of the vital signs that can be used for personal health monitoring is blood pressure. High BP is a common and dangerous condition. About 1 out of 3 adults in the U.S. (about 75 million people) have high BP. This common condition increases the risk of heart disease and stroke, two of the leading causes of death for Americans. High BP is called the silent killer because it often has no warning signs or symptoms, and many people are not aware they have it. For these reasons, it is important to develop a technology that can easily and conveniently check BP regularly. In biomedical data analysis, various studies are being attempted to effectively analyze by applying machine learning to biomedical big data accumulated in large quantities. However, collecting blood pressure-related data at the level of big data is very difficult and very expensive because it takes a lot of manpower and time. So in this dissertation, we proposed a three-step strategy to overcome these issues. First, we describe a BP prediction model with extraction and concentration CNN architecture, to process publicly disclosed sequential ECG and PPG dataset. Second, we evaluate the performance of the developed model by applying the developed model to privately measured data. To address the third issue, we propose the knowledge distillation method and input pre-processing method to improve the accuracy of the blood pressure prediction model. All the methods proposed in this dissertation are based on a deep convolutional neural network (CNN). Unlike other studies based on manual recognition of the features, by utilizing the advantage of deep learning which automatically extracts features, raw biomedical signals are used intact to reflect the inherent characteristics of the signals themselves.์ฝ”๋กœ๋‚˜ 19์— ์˜ํ•œ ์ „ ์„ธ๊ณ„์˜ ์‚ฌํšŒ์  ํ”„๋กœํ•„ ๋ณ€ํ™”๋กœ, ๊ทœ์ œ์™€ ์‹ ๋ขฐ์„ฑ์ด ๋‚ฎ๊ธฐ ๋•Œ๋ฌธ์— ํ™œ์„ฑํ™” ๋˜์ง€ ์•Š์€ ์›๊ฒฉ ์˜๋ฃŒ ๋ถ„์•ผ๋„ ํฐ ๋ณ€ํ™”๋ฅผ ๊ฒช์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋ฉ๋‹ˆ๋‹ค. ์ฝ”๋กœ๋‚˜ 19๊ฐ€ ๋ฏธ๊ตญ์— ํผ์ง์— ๋”ฐ๋ผ ๋ฏธ๊ตญ ๋ณด๊ฑด๋ณต์ง€๋ถ€๋Š” ์›๊ฒฉ ์ง„๋ฃŒ์˜ ํ‘œ์ค€์„ ์ผ์‹œ์ ์œผ๋กœ ์™„ํ™”ํ•˜๋ฉด์„œ ํŽ˜์ด์Šค๋ถ, ํŽ˜์ด์Šค๋ถ ๋ฉ”์‹ ์ € ๊ธฐ๋ฐ˜ ํ™”์ƒ ์ฑ„ํŒ…, ํ–‰์•„์›ƒ, ์Šค์นด์ดํ”„๋ฅผ ์‚ฌ์šฉํ•œ ์›๊ฒฉ ์ง„๋ฃŒ๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ–ˆ์Šต๋‹ˆ๋‹ค. ์›๊ฒฉ์˜๋ฃŒ ์‹œ์žฅ์˜ ํ™•์žฅ์€ ๊ธฐ์กด์˜ ์น˜๋ฃŒ์ค‘์‹ฌ ๋ณ‘์›์ฃผ๋„์˜ ์˜๋ฃŒ์‹œ์žฅ์„ ์›จ์–ด๋Ÿฌ๋ธ”, ๋น… ๋ฐ์ดํ„ฐ ๋ฐ ๊ฑด๊ฐ•๊ธฐ๋ก ๋ถ„์„์„ ํ†ตํ•œ ์˜ˆ๋ฐฉ ๋ฐ ๊ด€๋ฆฌ์— ์ค‘์ ์„ ๋‘” ๋””์ง€ํ„ธ ์˜๋ฃŒ ์„œ๋น„์Šค ์‹œ์žฅ์œผ๋กœ ๋น ๋ฅด๊ฒŒ ๋ณ€ํ™”์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์˜ˆ๋ฐฉ ๋ฐ ๊ด€๋ฆฌ ์ค‘์‹ฌ์˜ ๋””์ง€ํ„ธ ํ—ฌ์Šค์ผ€์–ด ์„œ๋น„์Šค์—์„œ๋Š” ์‚ฌ๋žŒ์˜ ๊ฑด๊ฐ• ์ƒํƒœ๋ฅผ ์‰ฝ๊ฒŒ ๋ชจ๋‹ˆํ„ฐ๋ง ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์ด ๋งค์šฐ ์ค‘์š”ํ•œ๋ฐ ํ˜ˆ์••์€ ๊ฐœ์ธ ๊ฑด๊ฐ• ๋ชจ๋‹ˆํ„ฐ๋ง์— ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ํ•„์ˆ˜ ์ง•ํ›„ ์ค‘ ํ•˜๋‚˜ ์ž…๋‹ˆ๋‹ค. ๊ณ ํ˜ˆ์••์€ ์•„์ฃผ ํ”ํ•˜๊ณ  ์œ„ํ—˜ํ•œ ์งˆํ™˜์ž…๋‹ˆ๋‹ค. ๋ฏธ๊ตญ ์„ฑ์ธ 3๋ช…์ค‘ 1๋ช…(์•ฝ 7,500๋งŒ๋ช…)์ด ๊ณ ํ˜ˆ์••์„ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ฏธ๊ตญ์ธ์˜ ์ฃผ์š” ์‚ฌ๋ง ์›์ธ ์ค‘ ๋‘๊ฐ€์ง€์ธ ์‹ฌ์žฅ์งˆํ™˜๊ณผ ๋‡Œ์กธ์ค‘์˜ ์œ„ํ—˜์„ ์ฆ๊ฐ€ ์‹œํ‚ต๋‹ˆ๋‹ค. ๊ณ ํ˜ˆ์••์€ ์‹ ์ฒด์— ๊ฒฝ๊ณ  ์‹ ํ˜ธ๋‚˜ ์ž๊ฐ ์ฆ์ƒ์ด ์—†์–ด ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์ด ์ž์‹ ์ด ๊ณ ํ˜ˆ์••์ธ ๊ฒƒ์„ ์ธ์ง€ํ•˜์ง€ ๋ชปํ•˜๊ธฐ ๋•Œ๋ฌธ์— "์‚ฌ์ผ๋ŸฐํŠธ ํ‚ฌ๋Ÿฌ"๋ผ ๋ถˆ๋ฆฌ์›๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์ •๊ธฐ์ ์œผ๋กœ ์‰ฝ๊ณ  ํŽธ๋ฆฌํ•˜๊ฒŒ ํ˜ˆ์••์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ์ด ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ์ƒ์ฒด์˜ํ•™ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ถ„์•ผ์—์„œ๋Š” ๋จธ์‹  ๋Ÿฌ๋‹์„ ๋Œ€๋Ÿ‰์œผ๋กœ ์ˆ˜์ง‘๋œ ์ƒ์ฒด์˜ํ•™ ๋น… ๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜๋Š” ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํšจ๊ณผ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋น… ๋ฐ์ดํ„ฐ ์ˆ˜์ค€์œผ๋กœ ๋‹ค๋Ÿ‰์˜ ํ˜ˆ์•• ๊ด€๋ จ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฒƒ์€ ๋งŽ์€ ์ „๋ฌธ์ ์ธ ์ธ๋ ฅ๋“ค์ด ์˜ค๋žœ์‹œ๊ฐ„์„ ํ•„์š”๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋งค์šฐ ์–ด๋ ต๊ณ  ๋น„์šฉ ๋˜ํ•œ ๋งŽ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ 3๋‹จ๊ณ„ ์ „๋žต์„ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ๋จผ์ € ๋ˆ„๊ตฌ๋‚˜ ์‹œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ณต๊ฐœ๋˜์–ด ์žˆ๋Š” ์‹ฌ์ „๋„, ๊ด‘์šฉ์ ๋งฅํŒŒ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉ, ์ˆœ์ฐจ์ ์ธ ์‹ฌ์ „๋„, ๊ด‘์šฉ์ ๋งฅํŒŒ ์‹ ํ˜ธ์—์„œ ํ˜ˆ์••์„ ์ž˜ ์˜ˆ์ธกํ•˜๋„๋ก ๊ณ ์•ˆ๋œ ์ถ”์ถœ ๋ฐ ๋†์ถ• ์ž‘์—…์„ ๋ฐ˜๋ณตํ•˜๋Š” ํ•จ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ ์ œ์•ˆ๋œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ๊ฐœ์ธ์—๊ฒŒ์„œ ์ธก์ •ํ•œ ๊ด‘์šฉ์ ๋งฅํŒŒ ์‹ ํ˜ธ๋ฅผ ์ด์šฉํ•ด ์ œ์•ˆ๋œ ํ•จ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ–ˆ์Šต๋‹ˆ๋‹ค. ์„ธ๋ฒˆ์งธ๋กœ ํ˜ˆ์••์˜ˆ์ธก ๋ชจ๋ธ์˜ ์ •ํ™•์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ง€์‹ ์ฆ๋ฅ˜๋ฒ•๊ณผ ์ž…๋ ฅ์‹ ํ˜ธ ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ๋ชจ๋“  ํ˜ˆ์••์˜ˆ์ธก ๋ฐฉ๋ฒ•์€ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ํ˜ˆ์•• ์˜ˆ์ธก์— ํ•„์š”ํ•œ ํŠน์ง•๋“ค์„ ์ˆ˜๋™์œผ๋กœ ์ถ”์ถœํ•ด์•ผ ํ•˜๋Š” ๋‹ค๋ฅธ ์—ฐ๊ตฌ๋“ค๊ณผ ๋‹ค๋ฅด๊ฒŒ ํŠน์ง•์„ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•˜๋Š” ๋”ฅ๋Ÿฌ๋‹์˜ ์žฅ์ ์„ ํ™œ์šฉ, ์•„๋ฌด๋Ÿฐ ์ฒ˜๋ฆฌ๋„ ํ•˜์ง€ ์•Š์€ ์›๋ž˜ ๊ทธ๋Œ€๋กœ์˜ ์ƒ์ฒด ์‹ ํ˜ธ์—์„œ ์‹ ํ˜ธ ์ž์ฒด์˜ ๊ณ ์œ ํ•œ ํŠน์ง•์„ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.1 Introduction 1 2 Background 5 2.1 Cuff-based BP measurement methods 9 2.1.1 Auscultatory method 9 2.1.2 Oscillometric method 10 2.1.3 Tonometric method 11 2.2 Biomedical signals used in cuffless BP prediction methods 13 2.2.1 Electrocardiography (ECG) 13 2.2.2 Photoplethysmography (PPG) 20 2.3 Cuffless BP measurement methods 21 2.3.1 PWV based BP prediction methods 25 2.3.2 Machine learning based pulse wave analysis methods 26 2.4 Deep learning for sequential biomedical data 30 2.4.1 Convolutional neural networks 31 2.4.2 Recurrent neural networks 32 3 End-to-end blood pressure prediction via fully convolutional networks 33 3.1 Introduction 35 3.2 Method 38 3.2.1 Data preparation 38 3.2.2 CNN based prediction model 41 3.2.3 Detailed architecture 45 3.3 Experimental results 47 3.3.1 Setup 47 3.3.2 Model evaluation & selection 48 3.3.3 Calibration-based method 51 3.3.4 Performance comparison 52 3.3.5 Verification using international standards for BP measurement grading criteria 54 3.3.6 Performance comparison by the input signal combinations 56 3.3.7 An ablation study of each architectural component of extraction-concentration blocks 58 3.3.8 Preprocessing of input signal to improve blood pressure prediction performance 59 3.4 Discussion 61 3.5 Summary 63 4 Blood pressure prediction by a smartphone sensor using fully convolutional networks 64 4.1 Introduction 66 4.2 Method 69 4.2.1 Data acquisition 71 4.2.2 Preprocessing of the PPG signals 71 4.2.3 PPG signal selection 71 4.2.4 Data preparation for CNN model training 72 4.2.5 Network architectures 72 4.3 Experimental results 75 4.3.1 Implementation details 75 4.3.2 Effect of PPG combination on BP prediction 75 4.3.3 Performance comparison with other related works 76 4.3.4 Verification using international standards for BP measurement grading criteria 77 4.3.5 Preprocessing of input signal to improve blood pressure prediction performance 79 4.4 Discussion 81 4.5 Summary 83 5 Improving accuracy of blood pressure prediction by distilling the knowledge of neural networks 84 5.1 Introduction 85 5.2 Methods 87 5.3 Experimental results 88 5.4 Discussion & Summary 89 6 Conclusion 90 6.1 Future work 92 Bibliography 93 Abstract (In Korean) 106Docto

    Cuffless Blood Pressure Estimation

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    The blood pressure is an important factor in the diagnosis and evaluation of several diseases, such as acute myocardial infarction and stroke. This way, continuous monitorization of this parameter is crucial to a correct health evaluation. The current methods, like the oscillometric method, have some major drawbacks, that can influence the output values or even make the measurements impossible. One example is the high frequency evaluation of the blood pressure, in the standard used methods the process of measuring can take up to 3 minutes, and a waiting time is necessary between consecutive measurements. This dissertation presents two different cuffless solution to solve those problems. One based on physical models of the human body, and the other using machine learning techniques. In the first solution seven models that correlate pulse transit time and blood pressure, deducted by different authors, were tested to evaluate which one performed better. The testes were performed in a custom dataset acquired at Fraunhofer AICOS and in clinical environment, with two different devices (low cost device and medical grade device). The results indicate that pulse transit time can be used to track blood pressure, the developed device/method was evaluated as grade A based in the Standard IEEE 1708-2014. The second solution itโ€™s a proof of concept using a public database and three different machine learning methods (Random Forest, Neural Network and AdaBoost). Two sets of features are calculated from the ECG and PPG signals, one using TSFEL (spectral, frequency and time domain features) and a total of 15 custom features. The proposed method outperforms the methods presented in bibliography with mean absolute error of 3.6 mmHg and 2.0 mmHg to systolic and diastolic blood pressure respectively

    The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

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    [EN] The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.This research received financial support from grants PID2021-00X128525-IV0, PID2021123804OB-I00 and TED2021-129996B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana.Cano, J.; Fรกcila, L.; Gracia-Baena, JM.; Zangrรณniz, R.; Alcaraz, R.; Rieta, JJ. (2022). The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography. Biosensors. 12(5):1-14. https://doi.org/10.3390/bios1205028911412
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