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      How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database

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          Abstract

          Cardiovascular disease (CVD) is the number one cause of non-infectious morbidity and mortality in the world. The detection, measurement, and management of high blood pressure play an essential role in the prevention and control of CVDs. However, owing to the limitations and discomfort of traditional blood pressure (BP) detection techniques, many new cuff-less blood pressure approaches have been proposed and explored. Most of these involve arterial wave propagation theory, which is based on pulse arrival time (PAT), the time interval needed for a pulse wave to travel from the heart to some distal place on the body, such as the finger or earlobe. For this study, the Medical Information Mart for Intensive Care (MIMIC) database was used as a benchmark for PAT analysis. Many researchers who use the MIMIC database make the erroneous assumption that all the signals are synchronized. Therefore, we decided to investigate the calculation of PAT intervals in the MIMIC database and check its usefulness for evaluating BP. Our findings have important implications for the future use of the MIMIC database, especially for BP evaluation.

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          Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring.

          Continuous blood pressure (BP) monitoring can provide invaluable information about individuals' health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This paper presents an efficient algorithm, based on the pulse arrival time (PAT), for the continuous and cuffless estimation of the systolic BP, diastolic blood pressure (DBP), and mean arterial pressure (MAP) values.
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            Optimal Signal Quality Index for Photoplethysmogram Signals

            A photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is typically collected by pulse oximeters. PPG signals collected via mobile devices are prone to artifacts that negatively impact measurement accuracy, which can lead to a significant number of misleading diagnoses. Given the rapidly increased use of mobile devices to collect PPG signals, developing an optimal signal quality index (SQI) is essential to classify the signal quality from these devices. Eight SQIs were developed and tested based on: perfusion, kurtosis, skewness, relative power, non-stationarity, zero crossing, entropy, and the matching of systolic wave detectors. Two independent annotators annotated all PPG data (106 recordings, 60 s each) and a third expert conducted the adjudication of differences. The independent annotators labeled each PPG signal with one of the following labels: excellent, acceptable or unfit for diagnosis. All indices were compared using Mahalanobis distance, linear discriminant analysis, quadratic discriminant analysis, and support vector machine with leave-one-out cross-validation. The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F 1 scores of 86.0%, 87.2%, and 79.1%, respectively.
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              Systolic Peak Detection in Acceleration Photoplethysmograms Measured from Emergency Responders in Tropical Conditions

              Photoplethysmogram (PPG) monitoring is not only essential for critically ill patients in hospitals or at home, but also for those undergoing exercise testing. However, processing PPG signals measured after exercise is challenging, especially if the environment is hot and humid. In this paper, we propose a novel algorithm that can detect systolic peaks under challenging conditions, as in the case of emergency responders in tropical conditions. Accurate systolic-peak detection is an important first step for the analysis of heart rate variability. Algorithms based on local maxima-minima, first-derivative, and slope sum are evaluated, and a new algorithm is introduced to improve the detection rate. With 40 healthy subjects, the new algorithm demonstrates the highest overall detection accuracy (99.84% sensitivity, 99.89% positive predictivity). Existing algorithms, such as Billauer's, Li's and Zong's, have comparable although lower accuracy. However, the proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination. For best performance, we show that a combination of two event-related moving averages with an offset threshold has an advantage in detecting systolic peaks, even in heat-stressed PPG signals.
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                11 March 2019
                March 2019
                : 8
                : 3
                : 337
                Affiliations
                [1 ]School of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada; liangyongbo001@ 123456gmail.com (Y.L.); rababw@ 123456ece.ubc.ca (R.W.)
                [2 ]School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide SA 5005, Australia; derek.abbott@ 123456adelaide.edu.au
                [3 ]Centre for Biomedical Engineering, The University of Adelaide, Adelaide SA 5005, Australia
                [4 ]Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK; newton.howard@ 123456nds.ox.ac.uk
                [5 ]Howard Brain Sciences Foundation, Providence, RI 02906, USA
                [6 ]Faculty of Medicine, University of British Columbia, Vancouver BC V1Y 1T3, Canada; klim@ 123456cw.bc.ca
                [7 ]BC Children’s & Women’s Hospital, Vancouver BC V6H 3N1, Canada
                Author notes
                [* ]Correspondence: moe.elgendi@ 123456gmail.com ; Tel.: +1-604-600-4139
                Author information
                https://orcid.org/0000-0002-0945-2674
                https://orcid.org/0000-0002-8503-3973
                https://orcid.org/0000-0002-2471-1902
                https://orcid.org/0000-0003-1831-0202
                Article
                jcm-08-00337
                10.3390/jcm8030337
                6462898
                30862031
                9c59b0eb-0b2b-4c84-bef7-c808391953d3
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 January 2019
                : 06 March 2019
                Categories
                Article

                pulse morphology,pulse oximeter,blood pressure monitoring,pulse arrival time,global health,digital medicine,wearable devices,hypertension assessment,hypertension evaluation

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