ORCID Profile
0000-0003-3790-2895
Current Organisations
The University of NSW
,
University of New South Wales
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Biomedical Engineering Not Elsewhere Classified | Biomedical Engineering | Other Behavioural And Cognitive Sciences | Applied Mathematics | Other Psychology and Cognitive Sciences | Systems Theory And Control | Population Trends And Policies | Social Policy And Planning | Information Systems Not Elsewhere Classified | Medical Devices | Public Health and Health Services | Sensor Technology (Chemical aspects) | Biomechanical Engineering | Decision Support and Group Support Systems | Medical Physics | Nanomaterials | Image Processing | Expert Systems | Stochastic Analysis and Modelling | Nanotechnology | Health Information Systems (incl. Surveillance) |
Medical instrumentation | The aged | Health related to ageing | Cardiovascular system and diseases | Communication services not elsewhere classified | Clinical health not specific to particular organs, diseases and conditions | Health Status (e.g. Indicators of Well-Being) | Integrated systems | Health policy economic outcomes | Productivity | Diagnostic Methods | Evaluation of Health Outcomes | Health Policy Economic Outcomes | Computer hardware and electronic equipment not elsewhere classified
Publisher: IOP Publishing
Date: 04-05-2017
Abstract: In this study we investigate inter-operator differences in determining systolic and diastolic pressure from auscultatory sound recordings of Korotkoff sounds. We introduce a new method to record and convert Korotkoff sounds to a high fidelity sound file which can be replayed under optimal conditions by multiple operators, for the independent determination of systolic and diastolic pressure points. We have developed a digitised data base of 643 NIBP records from 216 subjects. The Korotkoff signals of 310 good quality records were digitised and the Korotkoff sounds converted to high fidelity audio files. A randomly selected subset of 90 of these data files, were used by an expert panel to independently detect systolic and diastolic points. We then developed a semi-automated method of visualising processed Korotkoff sounds, supported by simple algorithms to detect systolic and diastolic pressure points that provided new insights on the reasons for large differences recorded by the expert panel. Detailed analysis of the 90 randomly selected records revealed that peak root mean square (RMS) energy of the Korotkoff sounds, ranged from 3.3 to 84 mV rms, with the lower bound below the audible range of 4-6 mV rms. The diastolic phase was below the minimum auditory threshold in only 47/90 records. This indicates that for approximately 50% of all records diastole could not be determined from Phase V silence. The maximum relative error recorded for systolic pressure between the two methods, auscultatory and visual/algorithmic, was 30.8 mmHg with a mean error of 8.0 ± 5.4 mmHg. We explore the impact of signal morphology and intensity of the Korotkoff sounds, as well as noise, cardiac arrhythmia and the hearing acuity of the operator, on the accuracy of the measurement. We conclude that large intra-personal variability in Korotkoff signal morphology and litudes, as well as variations in the hearing acuity of the operator, make accurate NIBP measurements using sphygmomanometry difficult and should not be used as the gold standard against which automated NIBP devices are calibrated. We propose an alternative method of visualizing the energy of the Korotkoff sounds and applying simple algorithms to determine systolic and diastolic pressure points, which whilst mimicking classical sphygmomanometry eliminates the problems associated with operator hearing acuity and complex and variable Korotkoff signal morphology.
Publisher: JMIR Publications Inc.
Date: 16-10-2017
Abstract: n a home telemonitoring trial, patient adherence with scheduled vital signs measurements is an important aspect that has not been thoroughly studied and for which data in the literature are limited. Levels of adherence have been reported as varying from approximately 40% to 90%, and in most cases, the adherence rate usually dropped off steadily over time. This drop is more evident in the first few weeks or months after the start. Higher adherence rates have been reported for simple types of monitoring and for shorter periods of intervention. If patients do not follow the intended procedure, poorer results than expected may be achieved. Hence, analyzing factors that can influence patient adherence is of great importance. he goal of the research was to present findings on patient adherence with scheduled vital signs measurements in the recently completed Commonwealth Scientific and Industrial Research Organisation (CSIRO) national trial of home telemonitoring of patients (mean age 70.5 years, SD 9.3 years) with chronic conditions (chronic obstructive pulmonary disease, coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) carried out at 5 locations along the east coast of Australia. We investigated the ability of chronically ill patients to carry out a daily schedule of vital signs measurements as part of a chronic disease management care plan over periods exceeding 6 months (302 days, SD 135 days) and explored different levels of adherence for different measurements as a function of age, gender, and supervisory models. n this study, 113 patients forming the test arm of a Before and After Control Intervention (BACI) home telemonitoring trial were analyzed. Patients were required to monitor on a daily basis a range of vital signs determined by their chronic condition and comorbidities. Vital signs included noninvasive blood pressure, pulse oximetry, spirometry, electrocardiogram (ECG), blood glucose level, body temperature, and body weight. Adherence was calculated as the number of days during which at least 1 measurement was taken over all days where measurements were scheduled. Different levels of adherence for different measurements, as a function of age, gender, and supervisory models, were analyzed using linear regression and analysis of covariance for a period of 1 year after the intervention. atients were monitored on average for 302 (SD 135) days, although some continued beyond 12 months. The overall adherence rate for all measurements was 64.1% (range 59.4% to 68.8%). The adherence rates of patients monitored in hospital settings relative to those monitored in community settings were significantly higher for spirometry (69.3%, range 60.4% to 78.2%, versus 41.0%, range 33.1% to 49.0%, P .001), body weight (64.5%, range 55.7% to 73.2%, versus 40.5%, range 32.3% to 48.7%, P .001), and body temperature (66.8%, range 59.7% to 73.9%, versus 55.2%, range 48.4% to 61.9%, P=.03). Adherence with blood glucose measurements (58.1%, range 46.7% to 69.5%, versus 50.2%, range 42.8% to 57.6%, P=.24) was not significantly different overall. Adherence rates for blood pressure (68.5%, range 62.7% to 74.2%, versus 59.7%, range 52.1% to 67.3%, P=.04), ECG (65.6%, range 59.7% to 71.5%, versus 56.5%, range 48.7% to 64.4%, P=.047), and pulse oximetry (67.0%, range 61.4% to 72.7%, versus 56.4%, range 48.6% to 64.1%, P=.02) were significantly higher in males relative to female subjects. No statistical differences were observed between rates of adherence for the younger patient group (70 years and younger) and older patient group (older than 70 years). atients with chronic conditions enrolled in the home telemonitoring trial were able to record their vital signs at home at least once every 2 days over prolonged periods of time. Male patients maintained a higher adherence than female patients over time, and patients supervised by hospital-based care coordinators reported higher levels of adherence with their measurement schedule relative to patients supervised in community settings. This was most noticeable for spirometry. ustralian New Zealand Clinical Trials Registry ACTRN12613000635763 www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030& isReview=true (Archived by WebCite at xPOU3DpR).
Publisher: Elsevier BV
Date: 06-2014
DOI: 10.1016/J.ARTMED.2014.05.002
Abstract: This study aims to develop an advanced portable remote monitoring system to supervise high intensity treadmill exercises. The supervisory level of the developed hierarchical system is implemented on a portable monitoring device (iPhone/iPad) as a client application, while the real-time control of treadmill exercises is accomplished by using an on-line adaptive neural network control scheme in a local computer system. During training or rehabilitation exercises, the intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient. In order to achieve adaptive tracking performance, a neural network controller has been designed and implemented. Six real-time experiments have been conducted to test the performance of the developed monitoring system. Experimental results obtained in real-time with heart-rate set-point varying from 145 bpm to 180 bmp, demonstrate that the proposed system can quickly and accurately regulate exercise intensity of treadmill running exercises with desired performance (no overshoot, settling time Ts ≤ 100 s). Subjects aged from 29 to 38 years old participated in different set-point experiments to confirm the system's adaptability to inter- and intra-model uncertainty. The desired system performance under external disturbances has also been confirmed in a final real-time experiment demonstrating a user carrying the 10 kg bag then removing it during the exercise. In contrast with conventional control approaches, the proposed adaptive controller achieves better heart rate tracking performance under inter- and intra-model uncertainty and external disturbances. The developed system can automatically adapt to various in idual exercisers and a range of exercise intensity.
Publisher: Elsevier BV
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 07-2018
Publisher: Walter de Gruyter GmbH
Date: 2016
Abstract: This article outlines a decision support system that seeks to help community nurses monitor the well-being of their chronically ill patients. It is designed for nurses to stay in contact with their patients without spending unnecessary time on less productive aspects of community nursing, such as avoidable driving to and from patients’ houses and taking measurements of vital signs to assess their health condition. It therefore allows the nurse to spend more time on managing the factors that could lead to a healthier patient. The decision support system is developed for two levels of mathematical capability. Nurses with a statistical background are provided with in-depth information allowing them to detect changes in mean, mean square error (and hence variation), and correlations using a variation on dynamic principle components. Less mathematically inclined nurses are offered information about trends, change points, and a simpler multivariate view of a patient’s well-being involving parallel coordinate plots.
Publisher: Association for Computing Machinery (ACM)
Date: 16-06-2020
DOI: 10.1145/3391613
Abstract: UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the design space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks.
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 08-2010
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 10-1992
Publisher: Springer Science and Business Media LLC
Date: 06-02-2014
Publisher: Springer Science and Business Media LLC
Date: 2014
Publisher: ACTAPRESS
Date: 2012
Publisher: IEEE
Date: 08-2007
Publisher: Informa UK Limited
Date: 18-10-2013
Publisher: Springer Science and Business Media LLC
Date: 03-2003
DOI: 10.1114/1.1553455
Abstract: When a brief current pulse is incident on excitable cells in cardiac and other nervous tissue, a change in phase of the cell's response is usually observed. In cardiac tissue, the cells are exposed to external stimulation of mainly positive currents, which depolarize the cells. We performed a systematic study of the effect of depolarizing stimuli, covering timing, magnitude, and duration, and demonstrated that all of these parameters influence the phase response of the cell. The phase response of our model cell compares favorably with measurements on isolated sinoatrial node cells. We investigated the phase response to single depolarizing stimuli as a function of the stimulus parameters (phase response curves), and then studied cell responses to the combined effect of a pulse train (entrainment phenomena). The range of magnitudes and durations for the stimuli were 0.01-5 nA and 0.01-50 ms. Comparisons of the entrainment properties of the model with experimental results show good agreement with similar modes and different entrainment ratios occurring for similar basic cycle lengths (as functions of the unperturbed cell period). Our results demonstrate that any combination of parameters that provide the same charge transfer to the cell causes a similar phase response, independent of the specific magnitude and duration for the entire range of stimuli investigated.
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 08-2014
Publisher: Springer Science and Business Media LLC
Date: 19-06-2017
DOI: 10.1038/NPHYS4167
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 12-2018
Publisher: ICST
Date: 2013
Publisher: Bentham Science Publishers Ltd.
Date: 28-05-2010
DOI: 10.2174/1874431101004020081
Abstract: Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise.
Publisher: Wiley
Date: 29-08-2019
DOI: 10.1002/RNC.4727
Publisher: Springer Science and Business Media LLC
Date: 25-09-2016
Publisher: Inderscience Publishers
Date: 2012
Publisher: Elsevier BV
Date: 05-1999
DOI: 10.1016/S1386-5056(98)00177-4
Abstract: A Web-based electrocardiogram (ECG) monitoring service in which a longitudinal clinical record is used for management of patients, is described. The Web application is used to collect clinical data from the patient's home. A database on the server acts as a central repository where this clinical information is stored. A Web browser provides access to the patient's records and ECG data. We discuss the technologies used to automate the retrieval and storage of clinical data from a patient database, and the recording and reviewing of clinical measurement data. On the client's Web browser, ActiveX controls embedded in the Web pages provide a link between the various components including the Web server, Web page, the specialised client side ECG review and acquisition software, and the local file system. The ActiveX controls also implement FTP functions to retrieve and submit clinical data to and from the server. An intelligent software agent on the server is activated whenever new ECG data is sent from the home. The agent compares historical data with newly acquired data. Using this method, an optimum patient care strategy can be evaluated, a summarised report along with reminders and suggestions for action is sent to the doctor and patient by email.
Publisher: IEEE
Date: 08-2016
Publisher: Springer Science and Business Media LLC
Date: 09-1976
DOI: 10.1007/BF02478046
Publisher: Inderscience Publishers
Date: 2012
Publisher: IOP Publishing
Date: 14-04-2022
Abstract: Objective . In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arterial blood pressure (IABP). Approach Estimating systolic (SBP) and diastolic blood pressure (DBP) from the presence of Korotkoff sounds as CP increases may eliminate these errors and give more accurate estimates of SBP relative to IABP readings. Main Results. In 63 subjects of varying age 45.4 ± 19.9 years (range 21–76 years), including 44 men (45.2 ± 19.5, range 21–76 years) and 19 women (45.6 ± 21.4, range 21 – 75 years), there was a significant ( p 0.0001) increase in SBP from 124.4 ± 15.7 to 129.2 ± 16.3 mmHg and a significant ( p 0.0001) increase in DBP from 70.2 ± 10.7 to 73.6 ± 11.5 mmHg. Of the 63 subjects, 59 showed a positive increase in SBP (1–19 mmHg) and 5 subjects showed a reduction (−5 to −1 mmHg). The average differences for SBP estimates derived as the cuff inflates and estimates derived as the cuff deflates were 4.9 ± 4.7 mmHg, not dissimilar to the differences observed between IABP and NIBP measurements. Although we could not develop multiparameter linear or nonlinear models to explain this phenomenon we have clearly demonstrated through analysis of variance test that both body mass index (BMI) and pulse wave velocity are implicated, supporting the hypothesis that the phenomenon is associated with age, higher BMI and stiffer arteries. Significance . The implications of this study are potentially profound requiring the implementation of a new paradigm for NIBP measurement and a revision of the international standards for their calibration.
Publisher: IEEE
Date: 08-2016
Publisher: Elsevier BV
Date: 07-2008
DOI: 10.1016/J.IJMEDINF.2007.10.006
Abstract: To identify any major factors that could affect patients' perceptions of a Home Telecare Management System (HTMS) and use the findings to contribute to development of a theoretical framework for patient acceptance of HTMS. Ten Focus Group Interviews (FGIs) were conducted with patients suffering from congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), or both, from seven different ethnic groups in Sydney. Six key discussion points were used to conduct the FGIs. The participants were shown a video demonstrating the HTMS and its operation, followed by the demonstration of an HTMS prototype. The participants, who had no prior experience with the HTMS, were then asked questions to access their perceptions in potentially real situations. The discussions were audio-taped and content analysis performed. Four major themes and 16 sub-themes were identified. The themes were: intention to use the HTMS, the impact of the HTMS on patients' health management, concerns associated with using the HTMS, and the impact of the HTMS on healthcare services. Most participants perceived the system as a useful and convenient mode of health care delivery, expressed positive attitudes toward the HTMS and expressed intent to use the system. However, there were concerns centred on the issues of cost, ease of use, clinical support, low self-efficacy and anxiety related to the use of the HTMS. The findings of this study suggest that HTMS self-efficacy and anxiety are likely to be important constructs in patients' acceptance of home telecare. Therefore, we propose these two factors be included in future HTMS acceptance models. Also it is suggested that in order to develop training programs for patients to use HTMS, tailored training components should aim to reduce 'HTMS anxiety' and improve 'HTMS self-efficacy'. Participants agreed that the HTMS would save cost and time by reducing hospital admissions, emergency department and medical practitioner visits and associated travel. Participants generally agreed that the HTMS could inform patients of their health conditions, thus promoting active participation in their health management and empowering them to perform better self-care. Also, they agreed that the HTMS could improve their health management by their doctors by providing more accurate and up-to-date information, to help them make better decisions. They suggested that the HTMS could have a preventative role in terms of providing early warning when their health conditions were deteriorating, which could lead to on-time appropriate interventions. The latter may result in reducing the use of emergency services and hospital admissions.
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 08-2010
Publisher: Elsevier BV
Date: 2016
Publisher: IEEE
Date: 08-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 07-2017
Publisher: Springer Science and Business Media LLC
Date: 03-06-2016
DOI: 10.1007/S11517-016-1530-9
Abstract: This paper is devoted to the problem of regulating the heart rate response along a predetermined reference profile, for cycle-ergometer exercises designed for training or cardio-respiratory rehabilitation. The controller designed in this study is a non-conventional, non-model-based, proportional, integral and derivative (PID) controller. The PID controller commands can be transmitted as biofeedback auditory commands, which can be heard and interpreted by the exercising subject to increase or reduce exercise intensity. However, in such a case, for the purposes of effectively communicating to the exercising subject a change in the required exercise intensity, the timing of this feedback signal relative to the position of the pedals becomes critical. A feedback signal delivered when the pedals are not in a suitable position to efficiently exert force may be ineffective and this may, in turn, lead to the cognitive disengagement of the user from the feedback controller. This note examines a novel form of control system which has been expressly designed for this project. The system is called an "actuator-based event-driven control system". The proposed control system was experimentally verified using 24 healthy male subjects who were randomly ided into two separate groups, along with cross-validation scheme. A statistical analysis was employed to test the generalisation of the PID tunes, derived based on the average transfer functions of the two groups, and it revealed that there were no significant differences between the mean values of root mean square of the tracking error of two groups (3.9 vs. 3.7 bpm, [Formula: see text]). Furthermore, the results of a second statistical hypothesis test showed that the proposed PID controller with novel synchronised biofeedback mechanism has better performance compared to a conventional PID controller with a fixed-rate biofeedback mechanism (Group 1: 3.9 vs. 5.0 bpm, Group 2: 3.7 vs. 4.4 bpm, [Formula: see text]).
Publisher: Wiley
Date: 12-10-2018
DOI: 10.1002/RNC.4376
Publisher: IEEE
Date: 08-2008
Publisher: American Chemical Society (ACS)
Date: 05-07-2017
Publisher: IEEE
Date: 2000
Publisher: IEEE
Date: 10-1992
Publisher: ACTAPRESS
Date: 2012
Publisher: JMIR Publications Inc.
Date: 03-07-2023
DOI: 10.2196/43154
Abstract: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence–based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist’s report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. PROSPERO CRD42021277155 www.crd.york.ac.uk rospero/display_record.php?RecordID=277155
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 08-2006
Publisher: Informa UK Limited
Date: 21-11-2017
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 02-2008
Publisher: IEEE
Date: 08-2015
Publisher: ACM
Date: 15-02-2014
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 08-2012
Publisher: Springer Science and Business Media LLC
Date: 16-08-2007
DOI: 10.1007/S10877-007-9086-8
Abstract: Traditional vital signs such as heart rate (HR) and blood pressure (BP) are often regarded as insensitive markers of mild to moderate blood loss. The present study investigated the feasibility of using pulse transit time (PTT) to track variations in pre-ejection period (PEP) during progressive central hypovolaemia induced by head-up tilt and evaluated the potential of PTT as an early non-invasive indicator of blood loss. About 11 healthy subjects underwent graded head-up tilt from 0 to 80 degrees . PTT and PEP were computed from the simultaneous measurement of electrocardiogram (ECG), finger photoplethysmographic pulse oximetry waveform (PPG-POW) and thoracic impedance plethysmogram (IPG). The response of PTT and PEP to tilt was compared with that of interbeat heart interval (RR) and BP. Least-squares linear regression analysis was carried out on an intra-subject basis between PTT and PEP and between various physiological variables and sine of the tilt angle (which is associated with the decrease in central blood volume) and the correlation coefficients (r) were computed. During graded tilt, PEP and PTT were strongly correlated in 10 out of 11 subjects (median r = 0.964) and had strong positive linear correlations with sine of the tilt angle (median r = 0.966 and 0.938 respectively). At a mild hypovolaemic state (20-30 degrees ), there was a significant increase in PTT and PEP compared with baseline (0 degrees ) but without a significant change in RR and BP. Gradient analysis showed that PTT was more responsive to central volume loss than RR during mild hypovolaemia (0-20 degrees ) but not moderate hypovolaemia (50-80 degrees ). PTT may reflect variation in PEP and central blood volume, and is potentially useful for early detection of non-hypotensive progressive central hypovolaemia. Joint interpretation of PTT and RR trends or responses may help to characterize the extent of blood volume loss in critical care patients.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 22-02-2011
DOI: 10.1007/S11517-011-0749-8
Abstract: Low frequency variability in the fingertip photoplethysmogram (PPG) waveform has been utilized for inferring sympathetic vascular control, but its relationship with a quantitative measure of vascular tone has not been established. In this study, we examined the association between fingertip PPG waveform variability (PPGV) and systemic vascular resistance (SVR) obtained from thermodilution cardiac output (CO) and intra-arterial pressure measurements in 48 post cardiac surgery intensive care unit patients. Among the hemodynamic measurements, both CO (P < 0.05) and SVR (P < 0.0001) had statistically significant relationships with the normalized low frequency power (LF(nu)) of PPGV. The LF(nu) of baseline PPGV had moderate but significant positive correlation with SVR (r = 0.54, P < 0.0001), and a value below 52.5 nu was able to identify SVR < 900 dyn s cm⁻⁵ with sensitivity of 59% and specificity of 95%. The results have provided quantitative evidence to confirm the link between fingertip PPGV and sympathetic vascular control. Suppression of LF vasomotor waves leading to dominance of respiration-related HF fluctuations in the fingertip circulation was a specific (though not sensitive) marker of systemic vasodilatation, which could be potentially utilized for the assessment of critical care patients.
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 08-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 07-2013
Publisher: CRC Press
Date: 14-08-2017
DOI: 10.1201/B22105
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 08-1972
DOI: 10.1038/238279A0
Publisher: Informa UK Limited
Date: 27-01-2017
Publisher: IEEE
Date: 06-2018
Publisher: Public Library of Science (PLoS)
Date: 06-08-2018
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 2001
Publisher: JMIR Publications Inc.
Date: 08-09-2017
Publisher: Springer Science and Business Media LLC
Date: 05-1991
DOI: 10.1007/BF02584303
Publisher: Association for Computing Machinery (ACM)
Date: 13-10-2021
DOI: 10.1145/3479547
Abstract: Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q& A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange.
Publisher: Wiley
Date: 11-04-2019
DOI: 10.1002/ACS.2992
Abstract: The aim of the study is to regulate the human heart rate (HR) response to a predefined reference profile during aerobic activities of unknown type. A novel feature of the designed control system is obtained to generate the desired rhythmic movements, which is required to achieve the target HR profile during aerobic activities of unknown type. These rhythmic movements or frequency of locomotion is known as the exercise rate (ER) and is quantified as a fundamental measure of exercise intensity. The relationship between ER and HR is modeled by using a linear time‐varying system. The parameters of the model are estimated using a Kalman filter. Based on this model, a robust adaptive H ∞ controller is designed. The H ∞ controller generates target ER (ER T ) corresponding to target HR (HR T ). This ER T is communicated to the exercising subject by using a human actuating system (HAS). The role of HAS is to achieve ER T . To validate the performance of the system, it is tested on six healthy subjects during rowing and cycling exercises. The results demonstrate that the designed control system can regulate HR at a given profile with an average root mean square error of 3.1857 bpm and 2.9396 bpm for rowing and cycling, respectively. The developed system can be used for designing an optimal exercising protocol for in iduals.
Publisher: IEEE
Date: 12-2016
Publisher: JMIR Publications Inc.
Date: 09-04-2018
Publisher: JMIR Publications Inc.
Date: 16-03-2018
Publisher: IEEE
Date: 08-2011
Publisher: Springer Science and Business Media LLC
Date: 16-02-2012
Abstract: Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Preliminary testing with 8 healthy in iduals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.
Publisher: IEEE
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Elsevier BV
Date: 2014
Publisher: JMIR Publications Inc.
Date: 16-10-2017
Publisher: Elsevier BV
Date: 10-2017
Publisher: IOP Publishing
Date: 30-03-2021
Abstract: Objective. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) based causal decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series. Here, we present the NA-MEMD based causal decomposition approach according to the covariation and power views traced to Hume and Kant: a priori cause-effect interaction is first acquired, and the presence of a candidate cause and of the effect is then computed from the sensory input somehow. Approach. Based on the definition of NA-MEMD based causal decomposition, we show such causal relation is a phase relation where the candidate causes are not merely followed by effects, but rather produce effects. Main results. The predominant methods used in neuroscience (Granger causality, empirical mode decomposition-based causal decomposition) are validated, showing the applicability of NA-MEMD based causal decomposition, particular to brain physiological processes in bivariate and multiscale time series. Significance. We point to the potential use in the causality inference analysis in a complex dynamic process.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 08-2006
Publisher: Springer Science and Business Media LLC
Date: 10-2008
DOI: 10.1007/S10877-008-9140-1
Abstract: Slow fluctuations in cardiovascular signals such as heart rate variability (HRV) are believed to carry important clinical information. This study investigated whether frequency spectrum analysis of the finger photoplethysmographic waveform variability (PPGV) could characterize a hypovolaemic response by using a blood donation as a model of controlled mild to moderate haemorrhage. This was a prospective, observational study carried out in a convenience s le of blood donors. Spectral analysis was performed on the finger infrared photoplethysmographic waveform and on the electrocardiogram- derived R-R intervals obtained from 43 healthy volunteers during blood donation. Spectral powers were calculated from low frequency (LF), mid frequency (MF) and high frequency (HF) bands of the spectrum of HRV and the coherence-weighted cross-spectrum of PPGV. Comparison was made between the four stages of blood donation: pre-donation (PRE), first half of donation (FIRST), second half of donation (SECOND) and post-donation (POST). A significant increase in the sum of the sympathetic-related MF and respiratory HF powers of finger PPGV (in mean-scaled units) was observed in SECOND and POST (P < 0.01). The post-donation increase in this PPGV spectral measure occurred in 77% of the subjects, which was higher than the percentage of subjects experiencing a blood pressure drop (71% or below). Normalized LF power of HRV showed a significant rise in SECOND (P < 0.01) but not in POST. Spectral analysis of finger PPGV may provide valuable information in addition to vital sign measurements in characterizing a hypovolaemic response. Given the limitations of the current blood loss model, further studies are required to assess the usefulness of finger PPGV for early haemorrhage detection in the clinical setting.
Publisher: Elsevier BV
Date: 09-1984
DOI: 10.1016/0165-1838(84)90080-8
Abstract: Regular physical activity has been shown to improve pregnancy outcomes. We sought to identify barriers to exercise during the first trimester of pregnancy. Five hundred forty-nine pregnant women in their first trimester rated barriers to exercise on a scale of 1 (
Publisher: IEEE
Date: 2000
Publisher: IOP Publishing
Date: 15-06-2017
Publisher: IOP Publishing
Date: 27-06-2011
DOI: 10.1088/0967-3334/32/8/012
Abstract: There is a need for robust techniques for early and accurate diagnosis of acute coronary syndromes (ACSs), to avoid inappropriate discharge of patients. This study examined the use of frequency spectrum analysis of heart rate variability (HRV) and photoplethysmogram (PPG) waveform variability for the identification of high-risk ACS patients defined by an elevated cardiac troponin level. The study cohort comprised a convenience s le of adult patients presenting to the emergency department of the Prince of Wales Hospital over a 4 month period complaining of non-traumatic chest pain. Valid electrocardiogram (ECG) and earlobe PPG waveforms together with troponin I test results were obtained from 52 patients at presentation, 4 of which were troponin I positive (Trop 0+). Frequency spectrum analysis was performed on the beat-to-beat HRV and PPG waveform variability (PPGV). The Trop 0+ were found to have significantly higher normalized mid-frequency power (MF(nu)) in HRV (P = 0.017), PPG litude variability (P = 0.009) and the cross-spectrum of HRV and PPGV (P = 0.001), which were attributed to reflex sympathetic response to myocardial ischemia. MF(nu) of PPG litude had the best overall performance in detecting Trop 0+, with ROC area under the curve of 0.93. The results demonstrate the potential use of ear PPG waveform to identify high-risk heart disease patients, and further highlight the utility of frequency spectrum analysis of PPGV in critical care.
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/696813
Abstract: Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that effective sound activity detection can produce significant gains in classification accuracy and that further gains can be made using source separation methods based on independent component analysis. Encouragingly, the results show that recognition accuracies in the range 70%–100% can be consistently obtained using different microphone-pair positions, under all but the most severe noise conditions.
Publisher: Springer Science and Business Media LLC
Date: 23-04-2018
Publisher: Wiley
Date: 12-02-2018
DOI: 10.1002/RNC.4061
Publisher: IOP Publishing
Date: 06-02-2009
DOI: 10.1088/0967-3334/30/3/001
Abstract: This study aims to quantitatively describe the steady-state relationships among percentage changes in key central cardiovascular variables (i.e. stroke volume, heart rate (HR), total peripheral resistance and cardiac output), measured using non-invasive means, in response to moderate exercise, and the oxygen uptake rate, using a new nonlinear regression approach-support vector regression. Ten untrained normal males exercised in an upright position on an electronically braked cycle ergometer with constant workloads ranging from 25 W to 125 W. Throughout the experiment, VO(2) was determined breath by breath and the HR was monitored beat by beat. During the last minute of each exercise session, the cardiac output was measured beat by beat using a novel non-invasive ultrasound-based device and blood pressure was measured using a tonometric measurement device. Based on the analysis of experimental data, nonlinear steady-state relationships between key central cardiovascular variables and VO(2) were qualitatively observed except for the HR which increased linearly as a function of increasing VO(2). Quantitative descriptions of these complex nonlinear behaviour were provided by nonparametric models which were obtained by using support vector regression.
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Springer Science and Business Media LLC
Date: 07-1993
DOI: 10.1007/BF02368625
Publisher: IEEE
Date: 2000
Publisher: IEEE
Date: 08-2007
Publisher: Springer Science and Business Media LLC
Date: 11-1989
DOI: 10.1007/BF02441641
Publisher: IEEE
Date: 08-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: IEEE
Date: 10-2014
Publisher: IMPERIAL COLLEGE PRESS
Date: 04-07-2012
Publisher: IMPERIAL COLLEGE PRESS
Date: 04-07-2012
Publisher: IEEE
Date: 10-1992
Publisher: Informa UK Limited
Date: 14-07-2017
Publisher: JMIR Publications Inc.
Date: 18-12-2017
Abstract: easonal variation has an impact on the hospitalization rate of patients with a range of cardiovascular diseases, including myocardial infarction and angina. This paper presents findings on the influence of seasonal variation on the results of a recently completed national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia. he aim is to evaluate the effect of the seasonal timing of hospital admission and length of stay on clinical outcome of a home telemonitoring trial involving patients (age: mean 72.2, SD 9.4 years) with chronic conditions (chronic obstructive pulmonary disease coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) and to explore methods of minimizing the influence of seasonal variations in the analysis of the effect of at-home telemonitoring on the number of hospital admissions and length of stay (LOS). atients were selected from a hospital list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered patients who had one or more admissions in the years from 2010 to 2012. Three different groups were analyzed separately because of substantially different climates: (1) Queensland, (2) Australian Capital Territory and Victoria, and (3) Tasmania. Time series data were analyzed using linear regression for a period of 3 years before the intervention to obtain an average seasonal variation pattern. A novel method that can reduce the impact of seasonal variation on the rate of hospitalization and LOS was used in the analysis of the outcome variables of the at-home telemonitoring trial. est patients were monitored for a mean 481 (SD 77) days with 87% (53/61) of patients monitored for more than 12 months. Trends in seasonal variations were obtained from 3 years’ of hospitalization data before intervention for the Queensland, Tasmania, and Australian Capital Territory and Victoria subgroups, respectively. The maximum deviation from baseline trends for LOS was 101.7% (SD 42.2%), 60.6% (SD 36.4%), and 158.3% (SD 68.1%). However, by synchronizing outcomes to the start date of intervention, the impact of seasonal variations was minimized to a maximum of 9.5% (SD 7.7%), thus improving the accuracy of the clinical outcomes reported. easonal variations have a significant effect on the rate of hospital admission and LOS in patients with chronic conditions. However, the impact of seasonal variation on clinical outcomes (rate of admissions, number of hospital admissions, and LOS) of at-home telemonitoring can be attenuated by synchronizing the analysis of outcomes to the commencement dates for the telemonitoring of vital signs. ustralian New Zealand Clinical Trial Registry ACTRN12613000635763 www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030& isReview=true (Archived by WebCite at www.webcitation.org/ 6xLPv9QDb)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: JMIR Publications Inc.
Date: 10-2022
Abstract: uberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence–based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. his systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). n conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. arious CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist’s report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. indings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. ROSPERO CRD42021277155 www.crd.york.ac.uk rospero/display_record.php?RecordID=277155
Start Date: 04-2003
End Date: 06-2008
Amount: $1,041,228.00
Funder: Australian Research Council
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End Date: 05-2020
Amount: $270,000.00
Funder: Australian Research Council
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End Date: 12-2013
Amount: $571,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2004
End Date: 12-2010
Amount: $787,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2004
End Date: 12-2004
Amount: $10,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2003
End Date: 06-2009
Amount: $615,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2004
End Date: 02-2007
Amount: $300,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2004
End Date: 06-2004
Amount: $30,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 11-2008
End Date: 12-2013
Amount: $460,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2022
End Date: 07-2027
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2004
End Date: 08-2010
Amount: $2,500,000.00
Funder: Australian Research Council
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