914 research outputs found

    Blood Pressure Estimation using Photoplethysmography only: Comparison between Different Machine Learning Approaches

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    Introduction: Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and photoplethysmograph (PPG) that makes them unsuitable for true wearable applications. Therefore, developing a single PPG based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods: The University of Queensland vital sign dataset (Online database) was accessed to extract raw PPG signals and its corresponding reference BPs (Systolic BP & Diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5s for each) were extracted, pre-processed and normalised in both width and amplitude. Three most significant features (Pulse area, Pulse Rising Time and Width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (Regression Tree, Multiple Linear Regression (MLR) and Support Vector Machine (SVM)). A 10-fold cross-validation was applied to obtain over-all BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (Normotensive, Hypertensive and Hypotensive). Finally, they were compared with the ISO standard for non-invasive BP device validation (average difference no greater than 5mmHg and SD no greater than 8mmHg). Results: In terms of overall measurement accuracy, the Regression Tree achieved the best overall accuracy for SBP (mean and SD of difference: -0.1±6.5mmHg) & DBP (mean and SD of difference: -0.6±5.2mmHg). MLR and SVM achieved the overall mean difference less than 5mmHg for both SBP and DBP but their SD of difference was >8mmHg. Regarding the measurement accuracy in each BP categories, only the Regression Tree achieved acceptable ISO standard for SBP (-1.1±5.7mmHg) & DBP (-0.03±5.6 mmHg) in the Normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion: This study developed and compared three machine learning algorithms to estimate BPs using PPG only, and revealed that the Regression Tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the Regression Tree algorithm achieved acceptable measurement accuracy only in the Normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories

    Experimental Demonstration of Accurate Noncontact Measurement of Arterial Pulse Wave Displacements Using 79-GHz Array Radar

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    In this study, we present a quantitative evaluation of the accuracy of simultaneous array-radar-based measurements of the displacements caused at two parts of the human body by arterial pulse wave propagation. To establish the feasibility of accurate radar-based noncontact measurement of this pulse wave propagation, we perform experiments with four participants using a 79-GHz millimeter-wave ultra-wideband multiple-input multiple-output array radar system and a pair of laser displacement sensors. We evaluate the accuracy of the pulse wave propagation measurements by comparing the displacement waveforms that are measured using the radar system with the corresponding waveforms that are measured using the laser sensors. In addition, to evaluate the estimates of the pulse wave propagation channels, we compare the impulse response functions that are calculated from the displacement waveforms obtained from both the radar data and the laser data. The displacement waveforms and the impulse responses both demonstrated the good agreement between the results of the radar and laser measurements. The normalized correlation coefficient between the impulse responses obtained from the radar and laser data on average was as high as 0.97 for the four participants. The results presented here strongly support the feasibility of accurate radar-based noncontact measurement of arterial pulse wave propagation

    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

    Novel Methods for Weak Physiological Parameters Monitoring.

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    M.S. Thesis. University of Hawaiʻi at Mānoa 2017

    Imaging photoplethysmography: towards effective physiological measurements

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    Since its conception decades ago, Photoplethysmography (PPG) the non-invasive opto-electronic technique that measures arterial pulsations in-vivo has proven its worth by achieving and maintaining its rank as a compulsory standard of patient monitoring. However successful, conventional contact monitoring mode is not suitable in certain clinical and biomedical situations, e.g., in the case of skin damage, or when unconstrained movement is required. With the advance of computer and photonics technologies, there has been a resurgence of interest in PPG and one potential route to overcome the abovementioned issues has been increasingly explored, i.e., imaging photoplethysmography (iPPG). The emerging field of iPPG offers some nascent opportunities in effective and comprehensive interpretation of the physiological phenomena, indicating a promising alternative to conventional PPG. Heart and respiration rate, perfusion mapping, and pulse rate variability have been accessed using iPPG. To effectively and remotely access physiological information through this emerging technique, a number of key issues are still to be addressed. The engineering issues of iPPG, particularly the influence of motion artefacts on signal quality, are addressed in this thesis, where an engineering model based on the revised Beer-Lambert law was developed and used to describe opto-physiological phenomena relevant to iPPG. An iPPG setup consisting of both hardware and software elements was developed to investigate its reliability and reproducibility in the context of effective remote physiological assessment. Specifically, a first study was conducted for the acquisition of vital physiological signs under various exercise conditions, i.e. resting, light and heavy cardiovascular exercise, in ten healthy subjects. The physiological parameters derived from the images captured by the iPPG system exhibited functional characteristics comparable to conventional contact PPG, i.e., maximum heart rate difference was <3 bpm and a significant (p < 0.05) correlation between both measurements were also revealed. Using a method for attenuation of motion artefacts, the heart rate and respiration rate information was successfully assessed from different anatomical locations even in high-intensity physical exercise situations. This study thereby leads to a new avenue for noncontact sensing of vital signs and remote physiological assessment, showing clear and promising applications in clinical triage and sports training. A second study was conducted to remotely assess pulse rate variability (PRV), which has been considered a valuable indicator of autonomic nervous system (ANS) status. The PRV information was obtained using the iPPG setup to appraise the ANS in ten normal subjects. The performance of the iPPG system in accessing PRV was evaluated via comparison with the readings from a contact PPG sensor. Strong correlation and good agreement between these two techniques verify the effectiveness of iPPG in the remote monitoring of PRV, thereby promoting iPPG as a potential alternative to the interpretation of physiological dynamics related to the ANS. The outcomes revealed in the thesis could present the trend of a robust non-contact technique for cardiovascular monitoring and evaluation

    Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients

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    Background: Non-invasive tracking of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBPIBP, DBPIBP, and MAPIBP, respectively) and to detect SBPIBP fluctuations. Methods: PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson’s correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBPIBP was determined by area under the curve (reported as AUC, sensitivity, specificity). Results: Significant correlations between 1/PTT and SBPIBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAPIBP/DBPIBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTTPES predicted a 30% SBPIBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBPIBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTTPCS detected a 30% SBPIBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTTPCS increase detected a 30% SBPIBP increase (0.73, 0.64, 0.68). Conclusions: Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBPIBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.German Government sponsored ZIM (Zentrales Innovationsprogramm Mittelstand) programPeer Reviewe

    Novel Machine Learning and Wearable Sensor Based Solutions for Smart Healthcare Monitoring

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    The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life and reduce the burden on caregivers and the cost of healthcare. Therefore, this thesis explores novel technological solutions for real-time monitoring of stress, anxiety, and blood pressure using unobtrusive wearable sensors and machine learning techniques. The first contribution of this thesis is the experimental data collection of 50 healthy older adults, based on which, the works on stress detection and anxiety detection have been developed. The data collection procedure lasted for more than a year. We have collected physiological signals, salivary cortisol, and self-reported questionnaire feedback during the study. Salivary cortisol is an established clinical biomarker for physiological stress. Hence, a stress detection model that is trained to distinguish between the stressed and not-stressed states as indicated by the increase in cortisol level has the potential to facilitate clinical level diagnosis of stress from the comfort of their own home. The second contribution of the thesis is the development of a stress detection model based on fingertip sensors. We have extracted features from Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) signals obtained from fingertip EDA and Photoplethysmogram (PPG) sensors to train machine learning algorithms for distinguishing between stressed and not-stressed states. We have evaluated the performance of four traditional machine learning algorithms and one deep-learning-based Long Short-Term Memory (LSTM) classifier. Results and analysis showed that the proposed LSTM classifier performed equally well as the traditional machine learning models. The third contribution of the thesis is to evaluate an integrated system of wrist-worn sensors for stress detection. We have evaluated four signal streams, EDA, BVP, Inter-Beat Interval (IBI), and Skin Temperature (ST) signals from EDA, PPG, and ST sensors. A random forest classifier was used for distinguishing between the stressed and not-stressed states. Results and analysis showed that incorporating features from different signals was able to reduce the misclassification rate of the classifier. Further, we have also prototyped the integration of the proposed wristband-based stress detection system in a consumer end device with voice capabilities. The fourth contribution of the thesis is the design of an anxiety detection model that uses features from a single wearable sensor and a context feature to improve the performance of the classification model. Using a context feature instead of integrating other physiological features for improving the performance of the model can reduce the complexity and cost of the anxiety detection model. In our proposed work, we have used a simple experimental context feature to highlight the importance of context in the accurate detection of anxious states. Our results and analysis have shown that with the addition of the context-based feature, the classifier was able to reduce misclassification by increasing the confidence of the decision. The final and the fifth contribution of the thesis is the validation of a proposed computational framework for the blood pressure estimation model. The proposed framework uses features from the PPG signal to estimate the systolic and diastolic blood pressure values using advanced regression techniques

    Robust Algorithms for Unattended Monitoring of Cardiovascular Health

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    Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

    Wearable and Nearable Biosensors and Systems for Healthcare

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    Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

    Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

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    Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien kĂ€yttö sydĂ€nkardiografiassa sekĂ€ lÀÀketieteellisessĂ€ 4D-kuvantamisessa Tausta: SydĂ€n- ja verisuonitaudit ovat yleisin kuolinsyy. NĂ€istĂ€ kuolemantapauksista lĂ€hes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron hĂ€iriöistĂ€. Moniulotteiset mikroelektromekaaniset jĂ€rjestelmĂ€t (MEMS) mahdollistavat sydĂ€nlihaksen mekaanisen liikkeen mittaamisen, mikĂ€ puolestaan tarjoaa tĂ€ysin uudenlaisen ja innovatiivisen ratkaisun sydĂ€men rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjĂ€rjestelmien kĂ€yttĂ€misen sydĂ€men toiminnan tutkimuksessa sekĂ€ lÀÀketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. MenetelmĂ€t: TĂ€mĂ€ vĂ€itöskirjatyö esittelee uuden sydĂ€men kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien kĂ€yttöön. Uudet laskennalliset lĂ€hestymistavat, jotka perustuvat signaalinkĂ€sittelyyn ja koneoppimiseen, mahdollistavat sydĂ€men patologisten hĂ€iriöiden havaitsemisen MEMS-antureista saatavista signaaleista. TĂ€ssĂ€ tutkimuksessa keskitytÀÀn erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). NĂ€iden tekniikoiden avulla voidaan mitata kardiorespiratorisen jĂ€rjestelmĂ€n mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, ettĂ€ integroimalla usean sensorin dataa voidaan mitata syketiheyttĂ€ 99% (terveillĂ€ n=29) tarkkuudella, havaita sydĂ€men rytmihĂ€iriöt (n=435) 95-97%, tarkkuudella, sekĂ€ havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). LisĂ€ksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydĂ€men 4D PET-kuvan laatua, kun liikeepĂ€tarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillĂ€, n=9) osoitti lupaavia tuloksia sydĂ€nsykkeen ajoituksen ja intervallien sekĂ€ sydĂ€nlihasmuutosten mittaamisessa. PÀÀtelmĂ€: TĂ€mĂ€n tutkimuksen tulokset osoittavat, ettĂ€ kardiologisilla MEMS-liikeantureilla on kliinistĂ€ potentiaalia sydĂ€men toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistÀÀ eteisvĂ€rinĂ€n (AFib), sydĂ€ninfarktin (MI) ja CAD:n havaitsemista. LisĂ€ksi MEMS-liiketunnistus parantaa sydĂ€men PET-kuvantamisen luotettavuutta ja laatua
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