ORCID Profile
0000-0003-1814-0856
Current Organisations
Mayo Clinic Rochester
,
Deakin University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Signal Processing | Biomedical Engineering not elsewhere classified | Medical Devices | Biomedical Engineering |
ANZSRC Socio-Economic Objective (SEO)
Diagnostic Methods | Expanding Knowledge in Engineering | Expanding Knowledge in Technology
Related Links
Publications
Effect of embedding dimension on complexity measures in identifying Arrhythmia
Publisher: IEEE
Date: 08-2016
Evaluation of Mesh and Sensor Resolution for Finite Element Modeling of Non-Invasive Fetal ECG Signals
Publisher: IEEE
Date: 11-2021
Influence of embedding dimension on distribution entropy in analyzing heart rate variability
Publisher: IEEE
Date: 08-2016
Sleep-Wake Classification using Statistical Features Extracted from Photoplethysmographic Signals
Publisher: IEEE
Date: 07-2019
Upper limb movement profiles during spontaneous motion in acute stroke
Publisher: IOP Publishing
Date: 04-2021
Abstract: Objective. The clinical assessment of upper limb hemiparesis in acute stroke involves repeated manual examination of hand movements during instructed tasks. This process is labour-intensive and prone to human error as well as being strenuous for the patient. Wearable motion sensors can automate the process by measuring characteristics of hand activity. Existing work in this direction either uses multiple sensors or complex instructed movements, or analyzes only the quantity of upper limb motion. These methods are obtrusive and strenuous for acute stroke patients and are also sensitive to noise. In this work, we propose to use only two wrist-worn accelerometer sensors to study the quality of completely spontaneous upper limb motion and investigate correlation with clinical scores for acute stroke care. Approach. The velocity time series estimated from acquired acceleration data during spontaneous motion is decomposed into smaller movement elements. Measures of density, duration and smoothness of these component elements are extracted and their disparity is studied across the two hands. Main results. Spontaneous upper limb motion in acute stroke can be decomposed into movement elements that resemble point-to-point reaching tasks. These elements are smoother and sparser in the normal hand than in the hemiparetic hand, and the amount of smoothness correlates with hemiparetic severity. Features characterizing the disparity of these movement elements between the two hands show statistical significance in differentiating mild-to-moderate and severe hemiparesis. Using data from 67 acute stroke patients, the proposed method can classify the two levels of hemiparetic severity with 85% accuracy. Additionally, compared to activity-based features, the proposed method is robust to the presence of noise in acquired data. Significance. This work demonstrates that the quality of upper limb motion can characterize and identify hemiparesis in stroke survivors. This is clinically significant towards the continuous automated assessment of hemiparesis in acute stroke using minimally intrusive wearable sensors.
Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG
Publisher: The Royal Society
Date: 08-2023
DOI: 10.1098/RSOS.221382
Abstract: The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors: and Hand Grip are studied on 32 healthy in iduals. Linear (Mean, s.d.) and nonlinear (Katz, Petrosian, Higuchi, S En, TotalS En) features are extracted from the PPG signal’s AC litudes to identify the onset, continuation and recovery phases of those stressors. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalS En feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgS En) are significant (AUC ≥ 0.8) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.
Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2009
Power spectral analysis for identifying the onset and termination of obstructive sleep apnoea events in ECG recordings
Publisher: IEEE
Date: 2008
Selection of Empirical Mode Decomposition Techniques for Extracting Breathing Rate From PPG
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection
Publisher: Elsevier BV
Date: 05-2020
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
Publisher: JMIR Publications Inc.
Date: 16-12-2016
DOI: 10.2196/JMIR.5870
Temporal dynamics of the circadian heart rate following low and high volume exercise training in sedentary male subjects
Publisher: Springer Science and Business Media LLC
Date: 21-05-2015
DOI: 10.1007/S00421-015-3185-X
Abstract: Increased risk of arrhythmic events occurs at certain times during the circadian cycle with the highest risk being in the second and fourth quarter of the day. Exercise improves treatment outcome in in iduals with cardiovascular disease. How different exercise protocols affect the circadian rhythm and the associated decrease in adverse cardiovascular risk over the circadian cycle has not been shown. Fifty sedentary male participants were randomized into an 8-week high volume and moderate volume training and a control group. Heart rate was recorded using Polar Electronics and investigated with Cosinor analysis and by Poincaré plot derived features of SD1, SD2 and the complex correlation measure (CCM) at 1-h intervals over the 24-h period. Moderate exercise significantly increased vagal modulation and the temporal dynamics of the heart rate in the second quarter of the circadian cycle (p = 0.004 and p = 0.007 respectively). High volume exercise had a similar effect on vagal output (p = 0.003) and temporal dynamics (p = 0.003). Cosinor analysis confirms that the circadian heart rate displays a shift in the acrophage following moderate and high volume exercise from before waking (1st quarter) to after waking (2nd quarter of day). Our results suggest that exercise shifts vagal influence and increases temporal dynamics of the heart rate to the 2nd quarter of the day and suggest that this may be the underlying physiological change leading to a decrease in adverse arrhythmic events during this otherwise high-risk period.
Multi-scale Tone Entropy in differentiating physiologic and synthetic RR time series
Publisher: IEEE
Date: 07-2013
A Self-Compassion Group Intervention for Patients Living With Chronic Medical Illness
Publisher: Physicians Postgraduate Press, Inc
Date: 26-09-2019
DOI: 10.4088/PCC.19M02470
Lateral decubitus posture during sleep: Sub-groups of obstructive sleep apnea patients - Therapeutic value of vertical position in OSA
Publisher: IEEE
Date: 12-2009
Photoplethysmographic-based automated sleep–wake classification using a support vector machine
Publisher: IOP Publishing
Date: 11-08-2020
Cross power spectral density between two-lead ECG signals at the termination of obstructive sleep apnea with or without arousals
Publisher: IEEE
Date: 09-2008
Interaction between sleep EEG and ECG signals during and after obstructive sleep apnea events with or without arousals
Publisher: IEEE
Date: 09-2008
Robust Heart Rate Estimation During Physical Exercise Using Photoplethysmographic Signals
Publisher: IEEE
Date: 07-2018
Robustness of electrocardiogram signal quality indices
Publisher: The Royal Society
Date: 04-2022
Abstract: Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQI p ), skewness (SQI skew ), signal-to-noise ratio (SQI snr ), higher order statistics SQI (SQI hos ) and peakedness of kurtosis (SQI kur ). We analysed the robustness of these SSQIs against different window sizes across erse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.
ECG Time Series Variability Analysis
Publisher: CRC Press
Date: 11-09-2017
Investigating scale invariant dynamics in minimum toe clearance variability of the young and elderly during treadmill walking
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals
Publisher: MDPI AG
Date: 21-05-2021
DOI: 10.3390/E23060642
Abstract: Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 s les were left in the CAD group and 438 s les in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.
Analyzing Systolic-Diastolic Interval Interaction Characteristics in Diabetic Cardiac Autonomic Neuropathy Progression
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Multi-lag tone–entropy in neonatal stress
Publisher: The Royal Society
Date: 09-2018
Abstract: Heart rate variability (HRV) has been analysed using linear and nonlinear methods. In the framework of a controlled neonatal stress model, we applied tone–entropy (T–E) analysis at multiple lags to understand the influence of external stressors on healthy term neonates. Forty term neonates were included in the study. HRV was analysed using multi-lag T–E at two resting and two stress phases (heel stimulation and a heel stick blood drawing phase). Higher mean entropy values and lower mean tone values when stressed showed a reduction in randomness with increased sympathetic and reduced parasympathetic activity. A ROC analysis was used to estimate the diagnostic performances of tone and entropy and combining both features. Comparing the resting and simulation phase separately, the performance of tone outperformed entropy, but combining the two in a quadratic linear regression model, neonates in resting as compared to stress phases could be distinguished with high accuracy. This raises the possibility that when applied across short time segments, multi-lag T–E becomes an additional tool for more objective assessment of neonatal stress.
Automatic recognition of obstructive sleep apnoea syndrome using power spectral analysis of electrocardiogram and Hidden Markov Models
Publisher: IEEE
Date: 12-2008
Measures of Bipedal Toe-Ground Clearance Asymmetry to Characterize Gait in Stroke Survivors
Publisher: IEEE
Date: 11-2021
Secondary measures of regularity from an entropy profile in detecting Arrhythmia
Publisher: IEEE
Date: 07-2017
Tone entropy analysis of foetal heart rate variability
Publisher: MDPI AG
Date: 02-03-2015
DOI: 10.3390/E17031042
Heart Rate Variability Alterations in Late Life Depression: A Meta-analysis
Publisher: Elsevier BV
Date: 08-2018
DOI: 10.1016/J.JAD.2018.04.071
Abstract: There is strong evidence for a bi-directional relationship between heart-health and depression in later life, but the physiological mechanisms underlying this relationship remain unclear. Heart rate variability is one promising factor that might help explain this relationship. We present results of a meta-analysis that considers heart rate variability alterations in older adults with depression. Literature search of Embase, PsychInfo and Medline revealed five clinical studies and six observational studies that examined the relationship between heart rate variability and depression in adults with a mean age over 60. These studies were included in this meta-analysis. Heart rate variability was reduced among older adults with clinical depression (N = 550), relative to healthy controls (Hedges' g = -0.334, 95%CI [-0.579, -0.090], p = .007). When high-frequency and low-frequency heart rate variability were investigated separately, only low-frequency heart rate variability was significantly reduced in depressed patients (Hedges' g = -0.626, 95%CI [-1.083, -0.169], p = .007). A similar but weaker pattern of results was found in the observational studies. Most findings remained significant among unmedicated depressed older adults. Evidence of effect-size heterogeneity was found in the clinical studies, indicating the need for more well-designed research in the area. Heart rate variability is reduced among older adults with depression, and this effect is not fully attributable to antidepressant medication use. Specifically, low-frequency heart rate variability may be reduced in depressed older adults. Heart rate variability warrants further attention, as it could help inform research into the prevention and treatment of depression in later life.
Toe clearance and velocity profiles of young and elderly during walking on sloped surfaces
Publisher: Springer Science and Business Media LLC
Date: 28-04-2010
Relationship between Heart Rate Variability and angiotensinogen gene polymorphism in diabetic and control individuals
Publisher: IEEE
Date: 08-2014
Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Effect of gender and diabetes on major depressive disorder using heart rate asymmetry
Publisher: IEEE
Date: 08-2014
Blockade of the angiotensin system improves mental health domain of quality of life: A meta-analysis of randomized clinical trials
Publisher: SAGE Publications
Date: 28-07-2017
Abstract: It is unclear whether blockade of the angiotensin system has effects on mental health. Our objective was to determine the impact of angiotensin converting enzyme inhibitors and angiotensin II type 1 receptor (AT1R) blockers on mental health domain of quality of life. Meta-analysis of published literature. PubMed and clinicaltrials.gov databases. The last search was conducted in January 2017. Randomized controlled trials comparing any angiotensin converting enzyme inhibitor or AT1R blocker versus placebo or non-angiotensin converting enzyme inhibitor or non-AT1R blocker were selected. Study participants were adults without any major physical symptoms. We adhered to meta-analysis reporting methods as per PRISMA and the Cochrane Collaboration. Eleven studies were included in the analysis. When compared with placebo or other antihypertensive medications, AT1R blockers and angiotensin converting enzyme inhibitors were associated with improved overall quality of life (standard mean difference = 0.11, 95% confidence interval = [0.08, 0.14], p < 0.0001), positive wellbeing (standard mean difference = 0.11, 95% confidence interval = [0.05, 0.17], p < 0.0001), mental (standard mean difference = 0.15, 95% confidence interval = [0.06, 0.25], p < 0.0001), and anxiety (standard mean difference = 0.08, 95% confidence interval = [0.01, 0.16], p < 0.0001) domains of QoL. No significant difference was found for the depression domain (standard mean difference = 0.05, 95% confidence interval = [0.02, 0.12], p = 0.15). Use of angiotensin blockers and inhibitors for the treatment of hypertension in otherwise healthy adults is associated with improved mental health domains of quality of life. Mental health quality of life was a secondary outcome in the included studies. Research specifically designed to analyse the usefulness of drugs that block the angiotensin system is necessary to properly evaluate this novel psychiatric target.
Multiscale entropy profiling to estimate complexity of heart rate dynamics
Publisher: American Physical Society (APS)
Date: 15-07-2019
Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal
Publisher: Springer Science and Business Media LLC
Date: 23-12-2016
Investigating foetal heart rate asymmetry
Publisher: IEEE
Date: 08-2014
A Novel Scalable Multi-class ROC for Effective Visualization and Computation
Publisher: Springer Berlin Heidelberg
Date: 2010
Remote health diagnosis and monitoring in the time of COVID-19
Publisher: IOP Publishing
Date: 10-2020
Abstract: Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.
An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal
Publisher: IEEE
Date: 08-2016
Modified Distribution Entropy as a Complexity Measure of Heart Rate Variability (HRV) Signal
Publisher: MDPI AG
Date: 24-09-2020
DOI: 10.3390/E22101077
Abstract: The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “mDistEn” a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.
Power spectral analysis of ECG signals during obstructive sleep apnoea hypopnoea epochs
Publisher: IEEE
Date: 2007
Estimating relative respiratory effort from features of photo-plethysmography signal
Publisher: IEEE
Date: 07-2013
Ensemble empirical mode decomposition with principal component analysis: a novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Does the Temporal Asymmetry of Short-Term Heart Rate Variability Change during Regular Walking? A Pilot Study of Healthy Young Subjects
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/3543048
Abstract: The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta’s index (PI), Guzik’s index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of ~0.03 Hz. Our pilot data showed a reduced PI but unchanged GI, SI, and AI during walking compared to resting seated position based on the raw data. Based on the wavelet-detrended data, reduced PI, SI, and AI were observed while GI still showed no significant changes. The reduced PI during walking based on both raw and detrended data which suggests less short-term HRA may underline the belief that vagal tone is withdrawn during low-intensity exercise. GI may not be sensitive to short-term HRA. The reduced SI and AI based on detrended data suggest that they may capture both short- and long-term HRA features and that the expected change in short-term HRA is lified after removing the trend that is supposed to link to long-term component. Further studies with more subjects and longer measurements are warranted to validate our observations and to examine these additional hypotheses.
Performance of a Convolutional Neural Network Derived from PPG Signal in Classifying Sleep Stages
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Data Pattern Recognition using Neural Network with Back-Propagation Training
Publisher: IEEE
Date: 12-2006
Performance Analysis of TCP Over Long-Delay Path and Algorithm for Improvement
Publisher: IEEE
Date: 12-2006
Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
The effects of asymmetric volume conductor modeling on non-invasive fetal ECG extraction
Publisher: IOP Publishing
Date: 11-2018
Abstract: Non-invasive fetal electrocardiography (NI-FECG) shows promise for capturing novel physiological information that may indicate signs of fetal distress. However, significant deterioration in NI-FECG signal quality occurs during the presence of a highly non-conductive layer known as vernix caseosa which forms on the fetal body surface beginning in approximately the 28th week of gestation. This work investigates asymmetric modeling of vernix caseosa and other maternal-fetal tissues in accordance with clinical observations and assesses their impacts for NI-FECG signal processing. We develop a process for simulating dynamic maternal-fetal abdominal ECG mixtures using a synthetic cardiac source model embedded in a finite element volume conductor. Using this process, changes in NI-FECG signal morphology are assessed in an extensive set of finite element models including spatially variable distributions of vernix caseosa. Our simulations show that volume conductor asymmetry can result in over 70% error in the observed T/QRS ratio and significant changes to signal morphology compared to a homogeneous volume conductor model. Volume conductor effects must be considered when analyzing T/QRS ratios obtained via NI-FECG and should be considered in future algorithm benchmarks using simulated data. This work shows that without knowledge of the influence of volume conductor effects, clinical evaluation of the T/QRS ratio derived via NI-FECG should be avoided.
Investigating the changes in heart rate asymmetry (HRA) with perturbation of parasympathetic nervous system
Publisher: Springer Science and Business Media LLC
Date: 12-2012
DOI: 10.1007/S13246-012-0173-X
Abstract: The heart rate asymmetry (HRA) is a disproportionate distribution of heart rate signal. The current study was designed to assess the changes in HRA in experimental conditions using Poincaré plot during parasympathetic blockade (atropine infusion) and parasympathetic enhancement (scopolamine administration). After atropine infusion, the heart rate variability in 5 out of 8 subjects was found asymmetric. In contrast, all 8 subjects were found to be asymmetric during scopolamine administration. The physiological relevance of HRA was demonstrated by showing correlation with standard frequency domain parameters during all phases of the experiment. The deviation of asymmetry index (GI ( p )) from symmetric range was further analyzed, which was maximum during scopolamine administration and minimum during atropine infusion. These findings suggest that parasympathetic block reduces the prevalence of HRA, and has significant correlation of GI ( p ) with frequency domain features of HRV analysis.
A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
Publisher: Frontiers Media SA
Date: 14-04-2016
Use of Ensemble Learning to Detect Buffer Overflow Exploitation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone–Entropy
Publisher: Springer Science and Business Media LLC
Date: 24-01-2013
DOI: 10.1007/S11517-012-1022-5
Abstract: Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone-Entropy (T-E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN-) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50-900) and lags (m: 1-8). The CAN- group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN- than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T-E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T-E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T-E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.
Multi-task transfer learning for in-hospital-death prediction of ICU patients
Publisher: IEEE
Date: 08-2016
Validation and Acceptability of a Cuffless Wrist-Worn Wearable Blood Pressure Monitoring Device Among Users and Health Care Professionals: Mixed Methods Study (Preprint)
Publisher: JMIR Publications Inc.
Date: 13-05-2019
Abstract: lood pressure (BP) is an important modifiable cardiovascular risk factor, yet its long-term monitoring remains problematic. Wearable cuffless devices enable the capture of multiple BP measures during everyday activities and could improve BP monitoring, but little is known about their validity or acceptability. his study aimed to validate a wrist-worn cuffless wearable BP device and assess its acceptability among users and health care professionals. mixed methods study was conducted to examine the validity and comparability of a wearable cuffless BP device against ambulatory and home devices. BP was measured simultaneously over 24 hours using wearable and ambulatory devices and over 7 days using wearable and home devices. Pearson correlation coefficients compared the degree of association between the measures, and limits of agreement (LOA Bland-Altman plots) were generated to assess measurement bias. Semistructured interviews were conducted with users and 10 health care professionals to assess acceptability, facilitators, and barriers to using the wearable device. Interviews were audio recorded, transcribed, and analyzed. total of 9090 BP measurements were collected from 20 healthy volunteers (mean 20.3 years, SD 5.4 N=10 females). Mean (SD) systolic BP (SBP)/diastolic BP (DBP) measured using the ambulatory (24 hours), home (7 days), and wearable (7 days) devices were 126 (SD 10)/75 (SD 6) mm Hg, 112 (SD 10)/71 (SD 9) mm Hg and 125 (SD 4)/77 (SD 3) mm Hg, respectively. Mean (LOA) biases and precision between the wearable and ambulatory devices over 24 hours were 0.5 (−10.1 to 11.1) mm Hg for SBP and 2.24 (−17.6 to 13.1) mm Hg for DBP. The mean biases (LOA) and precision between the wearable and home device over 7 days were −12.7 (−28.7 to 3.4) mm Hg for SBP and −5.6 (−20.5 to 9.2) mm Hg for DBP. The wearable BP device was well accepted by participants who found the device easy to wear and use. Both participants and health care providers agreed that the wearable cuffless devices were easy to use and that they could be used to improve BP monitoring. earable BP measures compared well against a gold-standard ambulatory device, indicating potential for this user-friendly method to augment BP management, particularly by enabling long-term monitoring that could improve treatment titration and increase understanding of users’ BP response during daily activity and stressors.
Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures
Publisher: Wiley
Date: 09-12-2019
DOI: 10.1111/EPI.14619
Abstract: To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES. In this study, motor data were recorded using wrist-worn ACM-based devices. A total of 83 clinical events were recorded: 39 generalized tonic-clonic seizures (GTCS) from 12 patients with epilepsy, and 44 convulsive PNES from 7 patients (one patient had both GTCS and PNES). The temporal variations in the ACM traces corresponding to 39 GTCS and 44 convulsive PNES events were extracted using Poincaré maps. Two new indices-tonic index (TI) and dispersion decay index (DDI)-were used to quantify the Poincaré-derived temporal variations for every GTCS and convulsive PNES event. The TI and DDI of Poincaré-derived temporal variations for GTCS events were higher in comparison to convulsive PNES events (P < 0.001). The onset and the subsiding patterns captured by TI and DDI differentiated between epileptic and convulsive nonepileptic seizures. An automated classifier built using TI and DDI of Poincaré-derived temporal variations could correctly differentiate 42 (sensitivity: 95.45%) of 44 convulsive PNES events and 37 (specificity: 94.87%) of 39 GTCS events. A blinded review of the Poincaré-derived temporal variations in GTCS and convulsive PNES by epileptologists differentiated 26 (sensitivity: 70.27%) of 44 PNES events and 33 (specificity: 86.84%) of 39 GTCS events correctly. In addition to quantifying the motor manifestation mechanism of GTCS and convulsive PNES, the proposed approach also has diagnostic significance. The new ACM features incorporate clinical characteristics of GTCS and PNES, thus providing an accurate, low-cost, and practical alternative to differential diagnosis of PNES.
Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series
Publisher: MDPI AG
Date: 20-12-2020
DOI: 10.3390/E22121439
Abstract: QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross s le entropy (XS En), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XS En, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.
Identifying increased risk of post-infarct people with diabetes using multi-lag Tone-Entropy analysis
Publisher: IEEE
Date: 08-2012
Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Understanding ageing effects by approximate entropy analysis of gait variability
Publisher: IEEE
Date: 08-2007
Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions
Publisher: Springer Science and Business Media LLC
Date: 27-08-2014
DOI: 10.1007/S11517-014-1188-0
Abstract: Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects' ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT-RR and a trivariate [Formula: see text] model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects' data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT-RR-RESP and QT-RR-EDR) gave significantly better fit value (p 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.
Lempel-Ziv complexity of the pNNx
Publisher: Elsevier BV
Date: 05-2021
Effect of data length and bin numbers on distribution entropy (DistEn) measurement in analyzing healthy aging
Publisher: IEEE
Date: 08-2015
Understanding ageing effects using complexity analysis of foot-ground clearance during walking
Publisher: Informa UK Limited
Date: 05-2013
DOI: 10.1080/10255842.2011.628943
Abstract: Ageing influences gait patterns which in turn can affect the balance control of human locomotion. Entropy-based regularity and complexity measures have been highly effective in analysing a broad range of physiological signals. Minimum toe clearance (MTC) is an event during the swing phase of the gait cycle and is highly sensitive to the spatial balance control properties of the locomotor system. The aim of this research was to investigate the regularity and complexity of the MTC time series due to healthy ageing and locomotors' disorders. MTC data from 30 healthy young (HY), 27 healthy elderly (HE) and 10 falls risk (FR) elderly subjects with balance problems were analysed. Continuous MTC data were collected and using the first 500 data points, MTC mean, standard deviation (SD) and entropy-based complexity analysis were performed using s le entropy (S En) for different window lengths (m) and filtering levels (r). The MTC S En values were lower in the FR group compared to the HY and HE groups for all m and r. The HY group had a greater mean S En value than both HE and FR reflecting higher complexity in their MTC series. The mean S En values of HY and FR groups were found significantly different for m = 2, 4, 5 and r = (0.1-0.9) × SD, (0.3-0.9) × SD and (0.3-0.9) × SD, respectively. They were also significant difference between HE and FR groups for m = 4-5 and r = (0.3-0.7) × SD, but no significant differences were seen between HY and HE groups for any m and r. A significant correlation of S En with SD of MTC was revealed for the HY and HE groups only, suggesting that locomotor disorders could significantly change the regularity or the complexity of the MTC series while healthy ageing does not. These results can be usefully applied to the early diagnosis of common gait pathologies.
Entropy Profiling to Detect ST Change in Heart Rate Variability Signals
Publisher: IEEE
Date: 07-2019
Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Poincaré plot methods for heart rate variability analysis
Publisher: Springer US
Date: 2013
Area asymmetry of heart rate variability signal
Publisher: Springer Science and Business Media LLC
Date: 21-09-2017
Phase asymmetry of heart rate variability signal
Publisher: IOP Publishing
Date: 14-01-2015
DOI: 10.1088/0967-3334/36/2/303
Abstract: Heart rate asymmetry (HRA) is considered as a physiological phenomenon in healthy subjects. In this article, we propose a novel HRA index, Slope Index (SI), to quantify phase asymmetry of heart rate variability (HRV) system. We assessed the performance of proposed index in comparison with conventional (Guzik's Index (GI) and Porta's Index (PI)) HRA indices. As illustrative ex les, we used two case studies: (i) differentiate physiologic RR series from synthetic RR series and (ii) discriminate arrhythmia subjects from Healthy using beat-to-beat heart rate time series. The results showed that SI is a superior parameter than GI and PI for both case studies with maximum ROC area of 0.84 and 0.82 respectively. In contrast, GI and PI had ROC areas {0.78, 0.61} and {0.50, 0.56} in two case studies respectively. We also performed surrogate analysis to show that phase asymmetry is caused by a physiologic phenomena rather than a random nature of the signal. In conclusion, quantification of phase asymmetry of HRV provides additional information on HRA, which might have a potential clinical use to discriminate pathological HRV in future.
Personalized Anatomic Modeling for Noninvasive Fetal ECG: Methodology and Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Tracing Software Exploitation
Publisher: Springer International Publishing
Date: 2021
Detection of generalized tonic-clonic seizures using short length accelerometry signal
Publisher: IEEE
Date: 07-2017
Increased beat-to-beat variation in diastolic phase percentages in patients with congestive heart failure
Publisher: IEEE
Date: 07-2017
Entropy Profiling for Detection of Fetal Arrhythmias in Short Length Fetal Heart Rate Recordings
Publisher: IEEE
Date: 07-2020
Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling
Publisher: IOP Publishing
Date: 28-02-2022
Abstract: Objective. Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings. Approach. Our method consists of extracting a fetal heart rate time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r . To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings. Main Results. We demonstrate that our method ( TotalS En ) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as S En (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification performance of TotalS En (AUC of 0.90). Significance. The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.
Modified thresholding technique of MMSPCA for extracting respiratory activity from short length PPG signal
Publisher: IEEE
Date: 07-2017
Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach
Publisher: Elsevier BV
Date: 04-2020
Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion
Publisher: IEEE
Date: 06-06-2021
Screening obstructive sleep apnoea syndrome from electrocardiogram recordings using support vector machines
Publisher: IEEE
Date: 09-2007
Detection of epileptic seizure based on entropy analysis of short-term EEG
Publisher: Public Library of Science (PLoS)
Date: 15-03-2018
Impacts of reference points and reference lines on the slope- and area-based heart rate asymmetry analysis
Publisher: Elsevier BV
Date: 04-2019
Multi-stage sleep classification using photoplethysmographic sensor
Publisher: The Royal Society
Date: 04-2023
DOI: 10.1098/RSOS.221517
Abstract: The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.
Onset Detection of Epileptic Seizures From Accelerometry Signal
Publisher: IEEE
Date: 07-2018
C-reactive protein concentrations across the mood spectrum in bipolar disorder: a systematic review and meta-analysis
Publisher: Elsevier BV
Date: 12-2016
Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings
Publisher: Elsevier BV
Date: 2009
DOI: 10.1016/J.COMPBIOMED.2008.11.003
Abstract: Patients with obstructive sleep apnoea syndrome (OSAS) are at increased risk of developing hypertension and other cardiovascular diseases. This paper explores the use of support vector machines (SVMs) for automated recognition of patients with OSAS types (+/-) using features extracted from nocturnal ECG recordings, and compares its performance with other classifiers. Features extracted from wavelet decomposition of heart rate variability (HRV) and ECG-derived respiration (EDR) signals of whole records (30 learning sets from physionet) are presented as inputs to train the SVM classifier to recognize OSAS+/- subjects. The optimal SVM parameter set is then determined by using a leave-one-out procedure. Independent test results have shown that an SVM using a subset of a selected combination of HRV and EDR features correctly recognized 30/30 of physionet test sets. In comparison, classification performance of K-nearest neighbour, probabilistic neural network, and linear discriminant classifiers on test data was lower. These results, therefore, demonstrate considerable potential in applying SVM in ECG-based screening and can aid sleep specialists in the initial assessment of patients with suspected OSAS.
Characterization of chimeric surface submentalis EMG activity during hypopneas in obstructive sleep apnea patients
Publisher: IEEE
Date: 09-2009
The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG
Publisher: IEEE
Date: 07-2019
Defining asymmetry in heart rate variability signals using a Poincaré plot
Publisher: IOP Publishing
Date: 08-10-2009
DOI: 10.1088/0967-3334/30/11/007
Abstract: The asymmetry in heart rate variability is a visibly obvious phenomenon in the Poincaré plot of normal sinus rhythm. It shows the unevenness in the distribution of points above and below the line of identity, which indicates instantaneous changes in the beat to beat heart rate. The major limitation of the existing asymmetry definition is that it considers only the instantaneous changes in the beat to beat heart rate rather than the pattern (increase/decrease). In this paper, a novel definition of asymmetry is proposed considering the geometry of a 2D Poincaré plot. Based on the proposed definition, traditional asymmetry indices--Guzik's index (GI), Porta's index (PI) and Ehlers' index (EI)--have been redefined. In order to compare the effectiveness of the new definition, all indices have been calculated for RR interval series of 54 subjects with normal sinus rhythm of 5 min and 30 min duration. The new definition resulted in a higher prevalence of normal subjects showing asymmetry in heart rate variability.
Methodological Comparisons of Heart Rate Variability Analysis in Patients With Type 2 Diabetes and Angiotensin Converting Enzyme Polymorphism
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Analyzing Distance Measures for Upper Limb Activity Measurement in Hemiparetic Stroke Patients
Publisher: IEEE
Date: 07-2020
Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Distribution Entropy (DistEn): A complexity measure to detect arrhythmia from short length RR interval time series
Publisher: IEEE
Date: 08-2015
A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool
Publisher: The Royal Society
Date: 06-2021
DOI: 10.1098/RSOS.202264
Abstract: We propose a novel machine learning-based method for analysing multi-night actigraphy signals to objectively classify and differentiate nocturnal awakenings in in iduals with chronic insomnia (CI) and their cohabiting healthy partners. We analysed nocturnal actigraphy signals from 40 cohabiting couples with one partner seeking treatment for insomnia. We extracted 12 time-domain dynamic and nonlinear features from the actigraphy signals to classify nocturnal awakenings in healthy in iduals and those with CI. These features were then used to train two machine learning classifiers, random forest (RF) and support vector machine (SVM). An optimization algorithm that incorporated the predicted quality of each night for each in idual was used to classify in iduals into CI or healthy sleepers. Using the proposed actigraphic signal analysis technique, coupled with a rigorous leave-one-out validation approach, we achieved a classification accuracy of 80% (sensitivity: 76%, specificity: 82%) in classifying CI in iduals and their healthy bed partners. The RF classifier (accuracy: 80%) showed a better performance than SVM (accuracy: 75%). Our approach to analysing the multi-night nocturnal actigraphy recordings provides a new method for screening in iduals with CI, using wrist-actigraphy devices, facilitating home monitoring.
Complex Correlation Measure: a novel descriptor for Poincaré plot
Publisher: Springer Science and Business Media LLC
Date: 2009
Unravelling unique qualitative and quantitative characteristics of the surface submentalis EMG in OSA polysomnograms
Publisher: IEEE
Date: 12-2010
Distribution entropy for short-term QT interval variability analysis: A comparison between the heart failure and normal control groups
Publisher: IEEE
Date: 09-2015
Design and Development of a Smartphone Application for Hypertension Management: An Intervention Mapping Approach (Preprint)
Publisher: JMIR Publications Inc.
Date: 02-10-2022
Abstract: everal research studies have demonstrated the potential of mobile health applications (apps) in supporting health management. However, the design and development process of these apps are rarely presented. e present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management. e used an intervention mapping approach for the development of theory- and evidence-based interventions for problem identification, problem-solving and mitigation strategies. This consisted of six fundamental steps of the intervention mapping approach: needs assessment, matrices, theoretical methods and practical strategies, program design, adoption and implementation plan, and evaluation plan. To design the contents of the intervention, we performed a literature review to determine the opinions and preferences of people with hypertension and implemented theoretical and practical strategies to support these needs in consultation with stakeholders and researchers. hrough the needs analysis, we identified that people with hypertension preferred having education, medication or treatment adherence, lifestyle modification, alcohol and smoking cessation and blood pressure monitoring support to manage their condition. Out of which, the authors utilized MoSCoW analysis to focus on four key elements, i.e., education, medication or treatment adherence, lifestyle modification and blood pressure support due to past experiences in developing interventions for hypertension, and its potential benefits in hypertension management. Theoretical models such as (i) the information, motivation, and behaviour skills (IMB) model, and (ii) the patient health engagement (PHE) model was implemented in the intervention development to ensure positive engagement and health behaviour. The app developed provides education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure support. The app also contains rules and medication lists titrated by the clinician to ensure treatment adherence, with regular push notifications to prompt behavioural change. In addition, the app data can be reviewed by patients and clinicians as needed. his is the first study describing the development of an app that integrates a wearable blood pressure device and provides lifestyle support and hypertension management. Our theory-driven intervention for self-management of hypertension is founded on the critical needs of people with hypertension to ensure treatment adherence and supports medication review and titration by clinicians. The intervention will be evaluated clinically in future studies to determine its effectiveness and usability.
A novel technique for analysing beat-to-beat dynamical changes of QT-RR distribution for arrhythmia prediction
Publisher: IEEE
Date: 09-2016
Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea
Publisher: Elsevier BV
Date: 03-2011
DOI: 10.1016/J.MEDENGPHY.2010.09.020
Abstract: We investigate whether pulse rate variability (PRV) extracted from finger photo-plethysmography (Pleth) waveforms can be the substitute of heart rate variability (HRV) from RR intervals of ECG signals during obstructive sleep apnea (OSA). Simultaneous measurements (ECG and Pleth) were taken from 29 healthy subjects during normal (undisturbed sleep) breathing and 22 patients with OSA during OSA events. Highly significant (p r>0.95) were found between heart rate (HR) and pulse rate (PR). Bland-Altman plot of HR and PR shows good agreement (<5% difference). Comparison of 2 min recording epochs demonstrated significant differences (p<0.01) in time, frequency domains and complexity analysis, between normal and OSA events using PRV as well as HRV measures. Results suggest that both HRV and PRV indices could be used to distinguish OSA events from normal breathing during sleep. However, several variability measures (SDNN, RMSSD, HF power, LF/HF and s le entropy) of PR and HR were found to be significantly (p<0.01) different during OSA events. Therefore, we conclude that PRV provides accurate inter-pulse variability to measure heart rate variability under normal breathing in sleep but does not precisely reflect HRV in sleep disordered breathing.
Identifying factors that conjointly influence nicotine vaping product relative harm perception among smokers and recent ex-smokers: Findings from the 2016 ITC Four Country Smoking and Vaping Survey
Publisher: Elsevier BV
Date: 2021
Effect of premature activation in analyzing QT dynamics instability using QT-RR model for ventricular fibrillation and healthy subjects
Publisher: IEEE
Date: 07-2013
Vedolizumab as induction and maintenance therapy for Crohn's disease.
Publisher: Massachusetts Medical Society
Date: 22-08-2013
Entropy Profiling: A Reduced—Parametric Measure of Kolmogorov—Sinai Entropy from Short-Term HRV Signal
Publisher: MDPI AG
Date: 10-12-2020
DOI: 10.3390/E22121396
Abstract: Entropy profiling is a recently introduced approach that reduces parametric dependence in traditional Kolmogorov-Sinai (KS) entropy measurement algorithms. The choice of the threshold parameter r of vector distances in traditional entropy computations is crucial in deciding the accuracy of signal irregularity information retrieved by these methods. In addition to making parametric choices completely data-driven, entropy profiling generates a complete profile of entropy information as against a single entropy estimate (seen in traditional algorithms). The benefits of using “profiling” instead of “estimation” are: (a) precursory methods such as approximate and s le entropy that have had the limitation of handling short-term signals (less than 1000 s les) are now made capable of the same (b) the entropy measure can capture complexity information from short and long-term signals without multi-scaling and (c) this new approach facilitates enhanced information retrieval from short-term HRV signals. The novel concept of entropy profiling has greatly equipped traditional algorithms to overcome existing limitations and broaden applicability in the field of short-term signal analysis. In this work, we present a review of KS-entropy methods and their limitations in the context of short-term heart rate variability analysis and elucidate the benefits of using entropy profiling as an alternative for the same.
Improved Detection and Classification of Convulsive Epileptic and Psychogenic Non-epileptic Seizures Using FLDA and Bayesian Inference
Publisher: IEEE
Date: 07-2018
Vedolizumab as induction and maintenance therapy for ulcerative colitis.
Publisher: Massachusetts Medical Society
Date: 22-08-2013
Wavelet-Based Multiscale Analysis of Minimum Toe Clearance Variability in the Young and Elderly during Walking
Publisher: IEEE
Date: 08-2007
Cross Entropy Profiling to Test Pattern Synchrony in Short-Term Signals
Publisher: IEEE
Date: 07-2019
Non-invasive sensor based automated smoking activity detection
Publisher: IEEE
Date: 07-2017
Detecting Subclinical Diabetic Cardiac Autonomic Neuropathy by Analyzing Ventricular Repolarization Dynamics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Performance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms
Publisher: IEEE
Date: 11-2021
Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal
Publisher: Frontiers Media SA
Date: 20-09-2017
Analyzing temporal variability of standard descriptors of Poincaré plots
Publisher: Elsevier BV
Date: 11-2010
DOI: 10.1016/J.JELECTROCARD.2010.09.001
Abstract: The Poincaré map is a visual technique to recognize the hidden correlation patterns of a time series signal. The standard descriptors of the Poincaré map are used to quantify the plot that measures the gross variability of the time series data. However, the problem lies in capturing temporal information of the plot quantitatively. In this article, we propose a new formulation for calculating the standard descriptors SD1 and SD2 from localized measures SD1^(w) and SD2^(w). To justify the importance of the temporal measure, SD1^(w), SD2^(w) are calculated for the 2 case studies (normal sinus rhythm [NSR] vs congestive heart failure and NSR vs arrhythmia) and are compared with the performance using the overall measures (SD1, SD2). Using overall SD1, receiver operating characteristic areas of 0.72 and 0.86 were obtained for NSR vs congestive heart failure and NSR vs arrhythmia, and using the proposed method resulted in 0.82 and 0.89. Because we have shown that the overall SD1 and SD2 are functions of the respective localized measures SD1^(w) and SD2^(w), we can conclude that use of localized measure provides equal or higher performance in pathology detection compared with the overall SD1 or SD2.
Effect of using ECG derived respiration (EDR) signal in linear parametric QT-RR modeling
Publisher: IEEE
Date: 07-2013
Sex-Based Differences in Incidence of Inflammatory Bowel Diseases—Pooled Analysis of Population-Based Studies From Western Countries
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1053/J.GASTRO.2018.06.043
Abstract: Although the incidence of inflammatory bowel diseases (IBDs) varies with age, few studies have examined variations between the sexes. We therefore used population data from established cohorts to analyze sex differences in IBD incidence according to age at diagnosis. We identified population-based cohorts of patients with IBD for which incidence and age data were available (17 distinct cohorts from 16 regions of Europe, North America, Australia, and New Zealand). We collected data through December 2016 on 95,605 incident cases of Crohn's disease (CD) (42,831 male and 52,774 female) and 112,004 incident cases of ulcerative colitis (UC) (61,672 male and 50,332 female). We pooled incidence rate ratios of CD and UC for the combined cohort and compared differences according to sex using random effects meta-analysis. Female patients had a lower risk of CD during childhood, until the age range of 10-14 years (incidence rate ratio, 0.70 95% CI, 0.53-0.93), but they had a higher risk of CD thereafter, which was statistically significant for the age groups of 25-29 years and older than 35 years. The incidence of UC did not differ significantly for female vs male patients (except for the age group of 5-9 years) until age 45 years thereafter, men had a significantly higher incidence of ulcerative colitis than women. In a pooled analysis of population-based studies, we found age at IBD onset to vary with sex. Further studies are needed to investigate mechanisms of sex differences in IBD incidence.
Heart rate independent QT variability component can detect subclinical cardiac autonomic neuropathy in diabetes
Publisher: IEEE
Date: 08-2016
The Influence of Pharmacological Autonomic Blockades on Multi-Scale Measures of Heart Rate Variability
Publisher: Springer Singapore
Date: 13-06-2017
Changes in heart rate circadian rhythm following exercise in middle-aged men
Publisher: IEEE
Date: 09-2015
Program Characterization for Software Exploitation Detection
Publisher: ACM
Date: 29-08-2023
Understanding Irregularity Characteristics of Short-Term HRV Signals Using Sample Entropy Profile
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Sensitivity of temporal heart rate variability in Poincaré plot to changes in parasympathetic nervous system activity
Publisher: Springer Science and Business Media LLC
Date: 03-03-2011
Abstract: A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes. This study was designed to assess the changes in temporal structure of the Poincaré plot using CCM during atropine infusion, 70° head-up tilt and scopolamine administration in healthy human subjects. CCM quantifies the point-to-point variation of the signal rather than gross description of the Poincaré plot. The physiological relevance of CCM was demonstrated by comparing the changes in CCM values with autonomic perturbation during all phases of the experiment. The sensitivities of short term variability ( SD 1), long term variability ( SD 2) and variability in temporal structure ( CCM ) were analyzed by changing the temporal structure by shuffling the sequences of points of the Poincaré plot. Surrogate analysis was used to show CCM as a measure of changes in temporal structure rather than random noise and sensitivity of CCM with changes in parasympathetic activity. CCM was found to be most sensitive to changes in temporal structure of the Poincaré plot as compared to SD 1 and SD 2. The values of all descriptors decreased with decrease in parasympathetic activity during atropine infusion and 70° head-up tilt phase. In contrast, values of all descriptors increased with increase in parasympathetic activity during scopolamine administration. The concordant reduction and enhancement in CCM values with parasympathetic activity indicates that the temporal variability of Poincaré plot is modulated by the parasympathetic activity which correlates with changes in CCM values. CCM is more sensitive than SD 1 and SD 2 to changes of parasympathetic activity.
PPG Derived Heart Rate Estimation During Intensive Physical Exercise
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records
Publisher: JMIR Publications Inc.
Date: 11-07-2016
DOI: 10.2196/MENTAL.5475
Abstract: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.
The Role of Serious Games in Robot Exoskeleton-Assisted Rehabilitation of Stroke Patients
Publisher: Springer International Publishing
Date: 2015
A novel technique to investigate the effect of ageing on ventricular repolarization characteristics in healthy and LQTS subjects
Publisher: IEEE
Date: 08-2015
Retinal artery and venular caliber grading: A semi-automated evaluation tool
Publisher: Elsevier BV
Date: 2014
DOI: 10.1016/J.COMPBIOMED.2013.07.018
Abstract: Retinal imaging can facilitate the measurement and quantification of subtle variations and abnormalities in retinal vasculature. Retinal vascular imaging may thus offer potential as a noninvasive research tool to probe the role and pathophysiology of the microvasculature, and as a cardiovascular risk prediction tool. In order to perform this, an accurate method must be provided that is statistically sound and repeatable. This paper presents the methodology of such a system that assists physicians in measuring vessel caliber (i.e., diameters or width) from digitized fundus photographs. The system involves texture and edge information to measure and quantify vessel caliber. The graphical user interfaces are developed to allow retinal image graders to select in idual vessel area that automatically returns the vessel calibers for noisy images. The accuracy of the method is validated using the measured caliber from graders and an existing method. The system provides very high accuracy vessel caliber measurement which is also reproducible with high consistency.
Drusen quantification for early identification of age related macular degeneration (AMD) using color fundus imaging
Publisher: IEEE
Date: 07-2013
Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation
Publisher: Elsevier BV
Date: 08-2015
Investigating relative respiratory effort signals during mixed sleep apnea using photoplethysmogram
Publisher: Springer Science and Business Media LLC
Date: 22-05-2013
Analysing effect of heart rate and age on radial artery pressure derived systolic and diastolic durations in healthy adults
Publisher: IEEE
Date: 09-2015
Influence of psychological stress on Systolic-Diastolic Interval (SDI) interaction characteristics measured from the electrocardiogram (ECG) signal
Publisher: IEEE
Date: 09-2015
Detection of Respiratory Arousals Using Photoplethysmography (PPG) Signal in Sleep Apnea Patients
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Validation and Acceptability of a Cuffless Wrist-Worn Wearable Blood Pressure Monitoring Device Among Users and Health Care Professionals: Mixed Methods Study
Publisher: JMIR Publications Inc.
Date: 14-09-2019
DOI: 10.2196/14706
Abstract: Blood pressure (BP) is an important modifiable cardiovascular risk factor, yet its long-term monitoring remains problematic. Wearable cuffless devices enable the capture of multiple BP measures during everyday activities and could improve BP monitoring, but little is known about their validity or acceptability. This study aimed to validate a wrist-worn cuffless wearable BP device (Model T2 TMART Technologies Limited) and assess its acceptability among users and health care professionals. A mixed methods study was conducted to examine the validity and comparability of a wearable cuffless BP device against ambulatory and home devices. BP was measured simultaneously over 24 hours using wearable and ambulatory devices and over 7 days using wearable and home devices. Pearson correlation coefficients compared the degree of association between the measures, and limits of agreement (LOA Bland-Altman plots) were generated to assess measurement bias. Semistructured interviews were conducted with users and 10 health care professionals to assess acceptability, facilitators, and barriers to using the wearable device. Interviews were audio recorded, transcribed, and analyzed. A total of 9090 BP measurements were collected from 20 healthy volunteers (mean 20.3 years, SD 5.4 N=10 females). Mean (SD) systolic BP (SBP)/diastolic BP (DBP) measured using the ambulatory (24 hours), home (7 days), and wearable (7 days) devices were 126 (SD 10)/75 (SD 6) mm Hg, 112 (SD 10)/71 (SD 9) mm Hg and 125 (SD 4)/77 (SD 3) mm Hg, respectively. Mean (LOA) biases and precision between the wearable and ambulatory devices over 24 hours were 0.5 (−10.1 to 11.1) mm Hg for SBP and 2.24 (−17.6 to 13.1) mm Hg for DBP. The mean biases (LOA) and precision between the wearable and home device over 7 days were −12.7 (−28.7 to 3.4) mm Hg for SBP and −5.6 (−20.5 to 9.2) mm Hg for DBP. The wearable BP device was well accepted by participants who found the device easy to wear and use. Both participants and health care providers agreed that the wearable cuffless devices were easy to use and that they could be used to improve BP monitoring. Wearable BP measures compared well against a gold-standard ambulatory device, indicating potential for this user-friendly method to augment BP management, particularly by enabling long-term monitoring that could improve treatment titration and increase understanding of users’ BP response during daily activity and stressors.
Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning
Publisher: Springer Science and Business Media LLC
Date: 24-05-2020
DOI: 10.1038/S41398-020-0836-4
Abstract: Precision psychiatry is attracting increasing attention lately as a recognized priority. One of the goals of precision psychiatry is to develop tools capable of aiding a clinically informed psychiatric diagnosis objectively. Cognitive, inflammatory and immunological factors are altered in both bipolar disorder (BD) and schizophrenia (SZ), however, most of these alterations do not respect diagnostic boundaries from a phenomenological perspective and possess great variability in different in iduals with the same phenotypic diagnosis and, consequently, none so far has proven to have the ability of reliably aiding in the differential diagnosis of BD and SZ. We developed a probabilistic multi-domain data integration model consisting of immune and inflammatory biomarkers in peripheral blood and cognitive biomarkers using machine learning to predict diagnosis of BD and SZ. A total of 416 participants, being 323, 372, and 279 subjects for blood, cognition and combined biomarkers analysis, respectively. Our multi-domain model performances for the BD vs. control (sensitivity 80% and specificity 71%) and for the SZ vs. control (sensitivity 84% and specificity 81%) pairs were high in general, however, our multi-domain model had only moderate performance for the differential diagnosis of BD and SZ (sensitivity 71% and specificity 73%). In conclusion, our results show that the diagnosis of BD and of SZ, and that the differential diagnosis of BD and SZ can be predicted with possible clinical utility by a computational machine learning algorithm employing blood and cognitive biomarkers, and that their integration in a multi-domain outperforms algorithms based in only one domain. Independent studies are needed to validate these findings.
Regulation of minimum toe clearance variability in the young and elderly during walking on sloped surfaces
Publisher: IEEE
Date: 08-2007
Distribution entropy analysis of epileptic EEG signals
Publisher: IEEE
Date: 08-2015
Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation
Publisher: Springer Berlin Heidelberg
Date: 2008
Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes
Publisher: IEEE
Date: 08-2015
Short-term QT interval variability in patients with coronary artery disease and congestive heart failure: a comparison with healthy control subjects
Publisher: Springer Science and Business Media LLC
Date: 25-08-2018
DOI: 10.1007/S11517-018-1870-8
Abstract: This study aimed to test how different QT interval variability (QTV) indices change in patients with coronary artery disease (CAD) and congestive heart failure (CHF). Twenty-nine healthy volunteers, 29 age-matched CAD patients, and 20 age-matched CHF patients were studied. QT time series were derived from 5-min resting lead-II electrocardiogram (ECG). Time domain indices [mean, SD, and QT variability index (QTVI)], frequency-domain indices (LF and HF), and nonlinear indices [s le entropy (S En), permutation entropy (PE), and dynamical patterns] were calculated. In order to account for possible influence of heart rate (HR) on QTV, all the calculations except QTVI were repeated on HR-corrected QT time series (QTc) using three correction methods (i.e., Bazett, Fridericia, and Framingham method). Results showed that CHF patients exhibited increased mean, increased SD, increased LF and HF, decreased T-wave litude, increased QTVI, and decreased PE, while showed no significant changes in S En. Interestingly, CHF patients also showed significantly changed distribution of the dynamical patterns with less monotonously changing patterns while more fluctuated patterns. In CAD group, only QTVI was found significantly increased as compared with healthy controls. Results after HR correction were in common with those before HR correction except for QTc based on Bazett correction. Graphical abstract Fig. The framework of this paper. The arrows show the sequential analysis of the data.
Interpretability and Optimisation of Convolutional Neural Networks Based on Sinc-Convolution
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
A Survey on Anomalous Behavior Detection for Elderly Care Using Dense-Sensing Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
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New Entropy Measures Of Short Term Signals For Smart Wearable Devices
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End Date: 2021
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Start Date: 05-2019
End Date: 05-2022
Amount: $351,000.00
Funder: Australian Research Council
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