ORCID Profile
0000-0002-0636-1646
Current Organisation
Khalifa University of Science and Technology
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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 | Biomedical Instrumentation | Cardiology (incl. Cardiovascular Diseases) | Foetal Development and Medicine | Pattern Recognition and Data Mining | Biomedical Engineering |
ANZSRC Socio-Economic Objective (SEO)
Diagnostic Methods | Medical Instruments | Expanding Knowledge in Engineering | Expanding Knowledge in the Medical and Health Sciences
Related Links
Publications
Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management (Preprint)
Publisher: JMIR Publications Inc.
Date: 09-04-2022
Abstract: ace masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various “smart” mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of in idual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.
Validation of beat by beat fetal heart signals acquired from four-channel fetal phonocardiogram with fetal electrocardiogram in healthy late pregnancy
Publisher: Springer Science and Business Media LLC
Date: 11-09-2018
DOI: 10.1038/S41598-018-31898-1
Abstract: Fetal heart rate monitoring is an essential obstetric procedure, however, false-positive results cause unnecessary obstetric interventions and healthcare cost. In this study, we propose a low cost and non-invasive fetal phonocardiography based signal system to measure the fetal heart sounds and fetal heart rate. Phonocardiogram (PCG) signals contain acoustic information reflecting the contraction and relaxation of the heart. We have developed a four-channel recording device with four separated piezoelectric sensors harnessed by a cloth sheet to record abdominal phonogram signals. A multi-lag covariance matrix based eigenvalue decomposition technique was used to extract fetal and maternal heart sounds as well as maternal breathing movement. In order to validate the fetal heart sounds extracted by PCG signal processing, 10 minutes’ simultaneous recordings of fetal Electrocardiogram (fECG) and abdominal phonogram from 15 pregnant women (27 ± 5-year-old) with fetal gestation ages between 33 and 40 weeks were obtained and processed. Highly significant (p 0.01) correlation (r = 0.96 N = 270) was found between beat to beat fetal heart rate (FHR ECG ) from fECG and the same (FHR PCG ) from fetal PCG signals. Bland–Altman plot of FHR ECG and FHR PCG shows good agreement ( % difference). We conclude that the proposed beat to beat fetal heart rate measurement system would be useful for monitoring fetal neurological wellbeing as a better alternative to traditional cardiotocogram based antenatal fetal heart rate monitoring.
Screening Cardiovascular Autonomic Neuropathy in Diabetic Patients With Microvascular Complications Using Machine Learning: A 24-Hour Heart Rate Variability Study
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2007
PULSE WAVE ANALYSIS USING TONE-ENTROPY ALGORITHM IN PEOPLE WITH AND WITHOUT FOOT COMPLAINTS IN A RURAL DIABETES SCREENING CLINIC
Publisher: ACTAPRESS
Date: 2010
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
A hybrid EMD-kurtosis method for estimating fetal heart rate from continuous doppler signals
Publisher: Frontiers Media SA
Date: 30-08-2017
Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Modeling respiratory movement signals during central and obstructive sleep apnea events using electrocardiogram
Publisher: Springer Science and Business Media LLC
Date: 23-10-2011
DOI: 10.1007/S10439-010-0189-X
Abstract: Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. CSA is recognized when respiratory effort falls below 15% of pre-event peak-to-peak litude of the respiratory effort. The aim of this study is to investigate whether a combination of respiratory sinus arrhythmia (RSA), ECG-derived respiration (EDR) from R-wave litudes and wavelet-based features of ECG signals during OSA and CSA can act as surrogate of changes in thoracic movement signal measured by respiratory inductance plethysmography (RIP). Therefore, RIP and ECG signals during 250 pre-scored OSA and 150 pre-scored CSA events, and 10 s preceding the events were collected from 17 patients. RSA, EDR, and wavelet decomposition of ECG signals at level 9 (0.15-0.32 Hz) were used as input to the support vector regression (SVR) model to recognize the RIP signals and classify OSA from CSA. Using cross-validation test, an optimal SVR (radial basis function kernel C = 2(8) and ε = 2(-2) where C is the coefficient for trade-off between empirical and structural risk and ε is the width of ε-insensitive region) showed that it correctly recognized 243/250 OSA and 139/150 CSA events (95.5% detection accuracy). Independent test was performed on 80 OSA and 80 CSA events from 12 patients. The independent test accuracies of OSA and CSA detections were found to be 92.5 and 95.0%, respectively. Results suggest superior performance of SVR using ECG as the surrogate in recognizing the reduction of respiratory movement during OSA and CSA. Results also indicate that ECG-based SVR model could act as a potential surrogate signal of respiratory movement during sleep-disordered breathing.
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
Evaluating cardiovascular risk using the tone-entropy algorithm
Publisher: IEEE
Date: 07-2013
Recognizing central and obstructive sleep apnea events from normal breathing events in ECG recordings
Publisher: IEEE
Date: 09-2008
Lateral decubitus posture during sleep: Sub-groups of obstructive sleep apnea patients - Therapeutic value of vertical position in OSA
Publisher: IEEE
Date: 12-2009
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
Comment on the article Prevalence of probable obstructive sleep apnea risk and severity in a population of dental patients by D.J. Levendowski et al
Publisher: Springer Science and Business Media LLC
Date: 04-03-2009
Revisiting Left Ventricular Ejection Fraction Levels: A Circadian Heart Rate Variability-Based Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Interaction between sleep EEG and ECG signals during and after obstructive sleep apnea events with or without arousals
Publisher: IEEE
Date: 09-2008
Automated Identification of fetal cardiac valve timings
Publisher: IEEE
Date: 07-2013
Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-03-2023
DOI: 10.36227/TECHRXIV.22258708.V1
Abstract: Peers’ conversation provides a domain of rich emotional information. The latter, apart from facial and gestural expressions, it is also naturally conveyed via peers’ speech, contributing to the establishment of a dynamic emotion climate (EC) during their conversational interaction. Recognition of EC could provide an additional source in understating peers’ social interaction and behavior on top of peers’ actual conversational content. Here, we propose a novel approach for speech-based EC recognition, namely AffECt, by combining peers’ complex affect dynamics (AD) with deep features extracted from speech signals using Temporary Convolutional Neural Networks (TCNNs). AffECt was tested and cross-validated on data drawn from there open datasets, i.e., K-EmoCon, IEMOCAP, and SEWA, in terms of EC arousal/valence level classification. The experimental results have shown that AffECt achieves EC classification accuracy up to 83.3% and 80.2% for arousal and valence, respectively, clearly surpassing the results reported in the literature, exhibiting robust performance across different languages. Moreover, there is a distinct improvement when the AD are combined with the TCNN, compared to the baseline deep learning approaches. These results demonstrate the effectiveness of AffECt in speech-based EC recognition, paving the way for many applications, e.g., in patients’ group therapy, negotiations, and emotion-aware mobile applications
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
Identifying depressed patients with and without suicidal ideation by finger photo-plethysmography
Publisher: IEEE
Date: 08-2016
Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG Patients
Publisher: Frontiers Media SA
Date: 2013
Cardiac rhythms of late pre-pipped and pipped chick embryos exposed to altered oxygen environments
Publisher: Elsevier BV
Date: 10-2003
DOI: 10.1016/S1095-6433(03)00171-5
Abstract: During the final stages of embryonic development in chickens, diffusive gas exchange through the chorioallantoic membrane (CAM) is progressively replaced by pulmonary respiration that begins with internal pipping (IP) of the CAM. Late chick embryos going through the transition from CAM respiration to pulmonary respiration were exposed to hyperoxic (100% O(2)) and hypoxic (10% O(2)/N(2)) environments for 2-h and the responses of baseline heart rate (HR), and HR fluctuation patterns were investigated. 16- and 18-day-old (referred to as 18-d) embryos and 20-d externally pipped (EP) embryos were examined as pre-pipped embryos and pipped embryos, respectively. 19-d embryos were ided into two groups: embryos that had not yet internally pipped (Pre-IP embryos) and embryos that had internally pipped (IP embryos). IP was identified by detecting the breathing signal with a condenser microphone attached hermetically on the eggshell (i.e. acoustorespirogram) on day 19 of incubation. In the hyperoxic environment, HR baseline of pre-pipped embryos remained unchanged and that of pipped embryos was depressed. In the hypoxic environment, HR baseline of 16-d pre-pipped embryos was depressed and that of pipped (IP and EP) embryos elevated. These different responses in pipped embryos might be partially attributed to increased cholinergic input from the vagus nerve in hyperoxia and increased adrenergic response in hypoxia. While hyperoxia did not induce marked modification of instantaneous heart rate (IHR) fluctuation patterns, hypoxia tended to augment transient decelerations of IHR in late pre-pipped embryos and markedly depressed HR fluctuations in pipped embryos.
Antepartum non-invasive evaluation of opening and closing timings of the cardiac valves in fetal cardiac cycle
Publisher: Springer Science and Business Media LLC
Date: 27-08-2009
DOI: 10.1007/S11517-009-0528-Y
Abstract: In this study, we propose a non-invasive algorithm to recognize the timings of fetal cardiac events on the basis of analysis of fetal ECG (FECG) and Doppler ultrasound signals. Multiresolution wavelet analysis enabled the frequency contents of the Doppler signals to be linked to the opening (o) and closing (c) of the heart's valves (Aortic (A) and Mitral (M)). M-mode, B-mode and pulsed Doppler ultrasound were used to verify the timings of opening and closure of these valves. In normal fetuses, the time intervals from Q-wave of QRS complex of FECG to opening and closing of aortic valve, i.e., Q-Ao and Q-Ac were found to be 79.3 +/- 17.4 and 224.7 +/- 13.3 ms, respectively. For the mitral valve, Q-Mc and Q-Mo were found to be 27.7 +/- 9.4 and 294.6 +/- 21.3 ms, respectively. Correlations among the timings in opening and closing of cardiac valves were found to be higher in abnormal fetuses than that in normal ones.
Lightweight Shufflenet Based CNN for Arrhythmia Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Analyzing Systolic-Diastolic Interval Interaction Characteristics in Diabetic Cardiac Autonomic Neuropathy Progression
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Automatic recognition of obstructive sleep apnoea syndrome using power spectral analysis of electrocardiogram and Hidden Markov Models
Publisher: IEEE
Date: 12-2008
Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool
Publisher: Cold Spring Harbor Laboratory
Date: 22-09-2021
DOI: 10.1101/2021.09.18.21263775
Abstract: This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein the relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.
Tone entropy analysis of foetal heart rate variability
Publisher: MDPI AG
Date: 02-03-2015
DOI: 10.3390/E17031042
Analysis of maternal–fetal heart rate coupling directions with partial directed coherence
Publisher: Elsevier BV
Date: 09-2016
Tone Entropy Analysis of Augmented Information Effects on Toe-Ground Clearance When Walking
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
A Comparative Study on Fetal Heart Rates Estimated from Fetal Phonography and Cardiotocography
Publisher: Frontiers Media SA
Date: 17-10-2017
Quantifying the interactions between maternal and fetal heart rates by transfer entropy
Publisher: Public Library of Science (PLoS)
Date: 23-12-2015
Privacy Aware Affective State Recognition From Visual Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
A Nano-Watt ECG Feature Extraction Engine in 65nm Technology
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
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
Effect of gender and diabetes on major depressive disorder using heart rate asymmetry
Publisher: IEEE
Date: 08-2014
Assessment of autonomic neurodevelopment in the mouse fetuses by using fetal electrocardiography
Publisher: IEEE
Date: 08-2016
Investigating foetal heart rate asymmetry
Publisher: IEEE
Date: 08-2014
Detecting fetal heart sounds by means of Fractal Dimension analysis in the Wavelet domain
Publisher: IEEE
Date: 07-2017
Association of cardiac autonomic neuropathy with alteration of sympatho-vagal balance through heart rate variability analysis
Publisher: Elsevier BV
Date: 03-2010
DOI: 10.1016/J.MEDENGPHY.2009.11.005
Abstract: Early sub-clinical assessment of severity of cardiac autonomic neuropathy (CAN) and intervention are of prime importance for risk stratification and early treatment in preventing sudden death due to silent myocardial infarction. The Ewing battery is currently the diagnostic tool of choice but is unable to detect sub-clinical disease and requires patient cooperation. Time and frequency domain analysis have several shortcomings including sensitivity to recording length, respiratory activity and non-stationarities in the ECG signal. An important step forward is to have a non-invasive method of detecting CAN that is robust against these shortcomings and has a higher sensitivity for the presence of both sub-clinical and overt clinical disease. This study presents a novel parameter, tone-entropy (T-E) that analyses heart rate variability (HRV) of 20 min lead II ECG recordings. Tone (T) represents sympatho-vagal balance and entropy (E) the autonomic regularity activity. Thirteen normal subjects without (CAN-) and 21 with CAN (CAN+) participated in this study. Among 21 CAN+ subjects, 13 are early CAN+ (eCAN+), eight are definite CAN+ (dCAN+) according to autonomic nervous system function tests as described by Ewing. The results showed that tone was higher and the entropy was lower in the dCAN+ group (T=-0.033 to -0.010 and E=1.73-2.24) compared with the eCAN+ (T=-0.0927 to -0.0311 and E=2.0-2.65) and normal (T=-0.128 to -0.0635 and E=2.64-3.15) group. The research verified that T-E is a suitable method to determine the presence of CAN that correctly identifies experimentally induced changes in cardiac function akin to parasympathetic and sympathetic dysfunction and differentiates between stages in CAN disease progression identified using the Ewing battery.
Investigating fetal myocardial function in heart anomalies by Doppler myocardial performance indices
Publisher: IEEE
Date: 07-2017
Enhanced Inter-subject Brain Computer Interface with Associative Sensorimotor Oscillations
Publisher: Institution of Engineering and Technology (IET)
Date: 02-2016
Power spectral analysis of ECG signals during obstructive sleep apnoea hypopnoea epochs
Publisher: IEEE
Date: 2007
Pulse Wave Analysis using Tone-Entropy Algorithm in People with and without Foot Complaints in a Rural Diabetics Screening Clinic
Publisher: ACTAPRESS
Date: 2011
Estimating relative respiratory effort from features of photo-plethysmography signal
Publisher: IEEE
Date: 07-2013
Adaptive technique for P and T wave delineation in electrocardiogram signals
Publisher: IEEE
Date: 08-2014
Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2009
Analysis between ECG and respiration signals in type II diabetic patients in the UAE
Publisher: IEEE
Date: 02-2014
Non-invasive extraction of fetal electrocardiogram using fast independent component analysis technique
Publisher: IEEE
Date: 02-2014
Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review
Publisher: JMIR Publications Inc.
Date: 15-11-2022
DOI: 10.2196/40797
Abstract: As a form of the Internet of Things (IoT)–gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86% EBSCO: 149/1268, 11.75% ScienceDirect: 248/1268, 19.55% and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.
Transfer entropy analysis of maternal and fetal heart rate coupling
Publisher: IEEE
Date: 08-2015
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.
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.
A comparative study on approximate entropy measure and poincaré plot indexes of minimum foot clearance variability in the elderly during walking
Publisher: Springer Science and Business Media LLC
Date: 02-02-2008
Investigating the beat by beat phase synchronization between maternal and fetal heart rates
Publisher: IEEE
Date: 07-2013
Adaptive ECG interval extraction
Publisher: IEEE
Date: 05-2015
QT Variability Index Changes With Severity of Cardiovascular Autonomic Neuropathy
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Identifying increased risk of post-infarct people with diabetes using multi-lag Tone-Entropy analysis
Publisher: IEEE
Date: 08-2012
Understanding ageing effects by approximate entropy analysis of gait variability
Publisher: IEEE
Date: 08-2007
A Nanowatt Real-Time Cardiac Autonomic Neuropathy Detector
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
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.
Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals
Publisher: Springer Science and Business Media LLC
Date: 22-12-2012
DOI: 10.1007/S10877-011-9323-Z
Abstract: Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5 s period. Eight level wavelet packet analysis was performed on each 5 s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing. The proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis. The classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals.
Estimating falls risk in the elderly: A wavelet based multiscale analysis
Publisher: IEEE
Date: 12-2007
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.
Poincaré plot methods for heart rate variability analysis
Publisher: Springer US
Date: 2013
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.
Fetal cardiac doppler signal processing techniques: Challenges and future research directions
Publisher: Frontiers Media SA
Date: 22-12-2017
Suicidal Ideation Is Associated with Altered Variability of Fingertip Photo-Plethysmogram Signal in Depressed Patients
Publisher: Frontiers Media SA
Date: 19-07-2017
Machine learning for designing an automated medical diagnostic system
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-887-1.CH030
Abstract: This chapter describes the application of machine learning techniques to solve biomedical problems in a variety of clinical domains. First, the concept of development and the main elements of a basic machine learning system for medical diagnostics are presented. This is followed by an introduction to the design of a diagnostic model for the identification of balance impairments in the elderly using human gait pattern, as well as a diagnostic model for predicating sleep apnoea syndrome from electrocardiogram recordings. Ex les are presented using support vector machines (a machine learning technique) to build a reliable model that utilizes key indices of physiological measurements (gait/electrocardiography [ECG] signals). A number of recommendations have been proposed for choosing the right classifier model in designing a successful medical diagnostic system. The chapter concludes with a discussion of the importance of signal processing techniques and other future trends in enhancing the performance of a diagnostic system.
Predicting depressed patients with suicidal ideation from ECG recordings
Publisher: Springer Science and Business Media LLC
Date: 18-08-2016
DOI: 10.1007/S11517-016-1557-Y
Abstract: Globally suicidal behavior is the third most common cause of death among patients with major depressive disorder (MDD). This study presents multi-lag tone-entropy (T-E) analysis of heart rate variability (HRV) as a screening tool for identifying MDD patients with suicidal ideation. Sixty-one ECG recordings (10 min) were acquired and analyzed from control subjects (29 CONT), 16 MDD subjects with (MDDSI+) and 16 without suicidal ideation (MDDSI-). After ECG preprocessing, tone and entropy values were calculated for multiple lags (m: 1-10). The MDDSI+ group was found to have a higher mean tone value compared to that of the MDDSI- group for lags 1-8, whereas the mean entropy value was lower in MDDSI+ than that in CONT group at all lags (1-10). Leave-one-out cross-validation tests, using a classification and regression tree (CART), obtained 94.83 % accuracy in predicting MDDSI+ subjects by using a combination of tone and entropy values at all lags and including demographic factors (age, BMI and waist circumference) compared to results with time and frequency domain HRV analysis. The results of this pilot study demonstrate the usefulness of multi-lag T-E analysis in identifying MDD patients with suicidal ideation and highlight the change in autonomic nervous system modulation of the heart rate associated with depression and suicidal ideation.
An Intelligent Algorithm for Home Sleep Apnea Test Device
Publisher: IGI Global
DOI: 10.4018/978-1-61520-977-4.CH011
Abstract: In this chapter authors try to develop a system for Sleep apnea with the help of machine learning algorithms using ECG signals.& nbsp The application of an intelligent machine learning technique (Support Vector Machines, SVM) to diagnose the patients with sleep apnea syndrome using Electrocardiogram (ECG) signal.& nbsp Sleep apnea syndrome is a medical condition caused by sleep apnea which is defined as the cessation of breathing for short periods during sleep.
Estimating Gestational Age From Maternal-Fetal Heart Rate Coupling Parameters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Mobile solutions for front-line health workers in developing countries
Publisher: IEEE
Date: 12-2009
Screening obstructive sleep apnoea syndrome from electrocardiogram recordings using support vector machines
Publisher: IEEE
Date: 09-2007
Care4MyHeart-PSG: A Personalized Serious Game Platform to Empower Phase III Cardiac Rehabilitation of Cardiovascular Disease Patients in UAE
Publisher: Springer International Publishing
Date: 2020
Development of respiratory rhythms in perinatal chick embryos
Publisher: Elsevier BV
Date: 04-2002
DOI: 10.1016/S1095-6433(02)00019-3
Abstract: In chick embryos, gas exchange takes place via the chorioallantoic membrane (CAM) and the lungs at approximately 1 day prior to hatching. The present study was designed to elucidate the development of respiratory rhythms in the chick embryo during the whole pipping (perinatal) period with a condenser-microphone measuring system. The microphone was hermetically attached on the eggshell over the air cell on day 18 of incubation. It first detected a cardiogenic signal (i.e. acoustocardiogram), and then beak clapping and breathing signals (acoustorespirogram, ARG). The first signals of lung ventilation appeared intermittently and irregularly approximately once per 5 s among the clapping signals after the embryo penetrated its beak into the air cell (internal pipping, IP). The respiratory rhythm then developed irregularly, with a subsequent more regular rate. The envelope pattern of breathing from the onset of IP through external pipping (EP) to hatching was constructed by a specially devised procedure, which eliminated external and internal noises. The envelope patterns indicated that the IP, EP and whole perinatal periods of 10 embryos were 14.1+/-6.4 (S.D.), 13.6+/-4.0 and 27.6+/-5.4 h, respectively. In addition, they also indicated the period of embryonic hatching activity (i.e. climax) which was 48+/-19 min. The development of respiratory rhythm was also shown by the instantaneous respiratory rate (IRR) which was designated as an inverse value of two adjacent ARG waves.
Model-based estimation of aortic and mitral valves opening and closing timings in developing human fetuses
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Reducing suicidal ideation by biofeedback-guided respiration – heart rate coherence
Publisher: Informa UK Limited
Date: 2020
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.
Designing a Low-Cost ECG Sensor and Monitor
Publisher: CRC Press
Date: 19-04-2011
DOI: 10.1201/B11195-14
Characterization of chimeric surface submentalis EMG activity during hypopneas in obstructive sleep apnea patients
Publisher: IEEE
Date: 09-2009
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
A hybrid Support Vector Machine and autoregressive model for detecting gait disorders in the elderly
Publisher: IEEE
Date: 08-2007
Heart Rate Asymmetry Analysis Using Poincaré Plot
Publisher: Springer US
Date: 2013
Analysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events
Publisher: IEEE
Date: 08-2008
Assessment of fetal development using cardiac valve intervals
Publisher: Frontiers Media SA
Date: 17-05-2017
Segmented Poincaré Plot Analysis and Lagged Segmented Poincaré Plot Analysis
Publisher: Springer US
Date: 2013
A continuous point measure for quantifying skull deformation in medical diagnostics
Publisher: Institution of Engineering and Technology (IET)
Date: 21-05-2014
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
A novel technique for analysing beat-to-beat dynamical changes of QT-RR distribution for arrhythmia prediction
Publisher: IEEE
Date: 09-2016
Identification of onset, maximum and termination of obstructive sleep apnoea events in single lead ECG recordings
Publisher: IEEE
Date: 08-2008
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.
Quantitative Poincaré Plot
Publisher: Springer US
Date: 2013
Poincaré Plot Interpretation of HRV Using Physiological Model
Publisher: Springer US
Date: 2013
Low cost ECG monitor for developing countries
Publisher: IEEE
Date: 12-2009
Poincaré Plot in Capturing Nonlinear Temporal Dynamics of HRV
Publisher: Springer US
Date: 2013
Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis
Publisher: Springer Science and Business Media LLC
Date: 2009
Effect of premature activation in analyzing QT dynamics instability using QT-RR model for ventricular fibrillation and healthy subjects
Publisher: IEEE
Date: 07-2013
Automated Estimation of Fetal Cardiac Timing Events From Doppler Ultrasound Signal Using Hybrid Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Wavelet-Based Multiscale Analysis of Minimum Toe Clearance Variability in the Young and Elderly during Walking
Publisher: IEEE
Date: 08-2007
Cortical response to psycho-physiological changes in auto-adaptive robot assisted gait training
Publisher: IEEE
Date: 08-2011
Detecting Subclinical Diabetic Cardiac Autonomic Neuropathy by Analyzing Ventricular Repolarization Dynamics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Association of cardiovascular risk using non-linear heart rate variability measures with the framingham risk score in a rural population
Publisher: Frontiers Media SA
Date: 2013
An autonomic cloud environment for hosting ECG data analysis services
Publisher: Elsevier BV
Date: 2012
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
One-Dimensional W-NETR for Non-Invasive Single Channel Fetal ECG Extraction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Heart rate independent QT variability component can detect subclinical cardiac autonomic neuropathy in diabetes
Publisher: IEEE
Date: 08-2016
Reduced variability in pulse wave velocity in depressed patients with suicidal ideation
Publisher: IEEE
Date: 09-2015
A biomedical SoC architecture for predicting ventricular arrhythmia
Publisher: IEEE
Date: 05-2016
New expression for base transit time in a bipolar transistor for all levels of injection
Publisher: Elsevier BV
Date: 2001
Predicting Hypertensive Patients With Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Assessing the development of fetal myocardial function by a novel Doppler myocardial performance index
Publisher: IEEE
Date: 08-2016
Changes in heart rate circadian rhythm following exercise in middle-aged men
Publisher: IEEE
Date: 09-2015
The Influence of Pharmacological Autonomic Blockades on Multi-Scale Measures of Heart Rate Variability
Publisher: Springer Singapore
Date: 13-06-2017
Non-invasive determination of electromechanical time intervals of cardiac cycle using abdominal ECG and doppler ultrasound signals from fetal hearts
Publisher: IEEE
Date: 09-2007
A multi-dimensional Hidden Markov Model approach to automated identification of fetal cardiac valve motion
Publisher: IEEE
Date: 08-2014
Explainable Computer-Aided Detection of Obstructive Sleep Apnea and Depression
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
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.
Investigating myocardial performance in normal and sick fetuses by abdominal Doppler signal derived indices
Publisher: Elsevier BV
Date: 2021
The Role of Serious Games in Robot Exoskeleton-Assisted Rehabilitation of Stroke Patients
Publisher: Springer International Publishing
Date: 2015
The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare
Publisher: Informa UK Limited
Date: 21-06-2023
A wavelet-based approach for screening falls risk in the elderly using support vector machines
Publisher: IEEE
Date: 12-2006
Smartphone-based low cost oximeter photoplethysmography
Publisher: IEEE
Date: 12-2010
A novel technique to investigate the effect of ageing on ventricular repolarization characteristics in healthy and LQTS subjects
Publisher: IEEE
Date: 08-2015
Evidence of Variabilities in EEG Dynamics during Motor Imagery-Based Multiclass Brain Computer Interface
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Investigating the effect of cholinergic and adrenergic blocking agents on maternal-fetal heart rates and their interactions in mice fetuses
Publisher: The Company of Biologists
Date: 13-04-2022
DOI: 10.1242/BIO.058999
Abstract: This study examines the role of autonomic control of maternal and fetal heart rate variability (MHRV and FHRV) and their heartbeats phase coupling prevalence (CPheartbeat) in mice. The subjects are ided into three groups: control with saline, cholinergic blockade with atropine, and β-adrenergic blockade with propranolol. Electrocardiogram signals of 27 anesthetized pregnant mice and 48 fetuses were measured for 20 min (drugs were administered after 10 min). For the coupling analysis, different maternal heartbeats were considered for one fetal beat. Results show that saline infusion did not produce any significant changes in MHRV and FHRV, as well as CPheartbeat. Atropine increased maternal HR (MHR) and decreased MHRV significantly without any considerable effect on fetal HR (FHR) and FHRV. Propranolol infusion did not produce any significant changes in MHR and MHRV, but significantly decreased FHR and increased FHRV. Moreover, atropine had led to a decrease in CPheartbeat when considering two and three maternal beats, and an increase for four beats while propranolol resulted in a decrease for two heartbeats, but an increase for four and five beats. The proposed approach is useful for assessing the impact of maternal autonomic modulation activity on fetal distress and obstetric complications prevalent in pregnant mothers.
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
Classification of Doppler Ultrasound signal quality for the application of fetal valve motion identification
Publisher: IEEE
Date: 09-2016
Non-invasive evaluation of opening and closing timings of the cardiac valves in the fetal cardiac cycle
Publisher: IEEE
Date: 09-2008
Heart Sound Segmentation toward Automated Heart Murmur Classification in Pediatric Patents
Publisher: IEEE
Date: 11-2016
DOI: 10.1109/SIP.2015.11
Variations in the accuracy of the ECG based detection of obstructive sleep apnoea (OSA) for different numbers of ECG leads and categories of OSA events
Publisher: IEEE
Date: 08-2008
Cochlear Transform
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 28-08-2023
DOI: 10.36227/TECHRXIV.24010356.V1
Abstract: Progress in signal processing seems to converge towards perceptually inspired algorithms. This work proposes a novel signal processing framework, namely Cochlear Transform (CT), inspired by the active spiral cochlear mechanics. CT reflects how the spiral cochlea transforms the input stimuli to spatially supported and tonotopically organized basilar membrane responses, resulting in orthogonal cochlear modes with bio-inspired Time-Frequency Representation (TFR). We treat an input signal as a propagating wave that travels from the cochlear base to apex, along a path that takes a spiral shape from θ = 0◦ to θ = 990◦, respectively. More importantly, we showcase the generalizability of the CT to non-acoustic signals by modulating them to the highest sensitivity region of the cochlea.We try to achieve balance between bio-inspiration and adaptive signal processing. Accordingly, we demonstrate that CT attains important characteristics, such as filterbank with perceptual tuning when excited by White Gaussian Noise, noise robustness, and the cochlear decomposition orthogonality. Numerical ex les of synthetic and real signals are given to demonstrate the robustness of CT and comparisons are made with Empirical Wavelet Transform (EWT) and the Variational Mode Decomposition (VMD). CT reveals new unseen and interpretable features not only of acoustic signals, but also real-life biological and seismic signals that stem from the active cochlear mechanics including acoustic distortion effects. This study is the first to demonstrate that mimicking both the active function and the structure of the human cochlear results inenhanced and interpretable TFR resolution, which allows the generalization of the approach to non-acoustic signals. CT resembles spectro-temporal receptive fields necessary for downstream perceptual auditory tasks with a range of application in data analysis, enhancing synergies between machine intelligence and bio-inspired perception. It also paves the way for bio-inspired feature engineering methods, including future CT extensions to applications for multivariate signals and 2D image processing.
Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-03-2023
DOI: 10.36227/TECHRXIV.22258708
Abstract: Peers’ conversation provides a domain of rich emotional information. The latter, apart from facial and gestural expressions, it is also naturally conveyed via peers’ speech, contributing to the establishment of a dynamic emotion climate (EC) during their conversational interaction. Recognition of EC could provide an additional source in understating peers’ social interaction and behavior on top of peers’ actual conversational content. Here, we propose a novel approach for speech-based EC recognition, namely AffECt, by combining peers’ complex affect dynamics (AD) with deep features extracted from speech signals using Temporary Convolutional Neural Networks (TCNNs). AffECt was tested and cross-validated on data drawn from there open datasets, i.e., K-EmoCon, IEMOCAP, and SEWA, in terms of EC arousal/valence level classification. The experimental results have shown that AffECt achieves EC classification accuracy up to 83.3% and 80.2% for arousal and valence, respectively, clearly surpassing the results reported in the literature, exhibiting robust performance across different languages. Moreover, there is a distinct improvement when the AD are combined with the TCNN, compared to the baseline deep learning approaches. These results demonstrate the effectiveness of AffECt in speech-based EC recognition, paving the way for many applications, e.g., in patients’ group therapy, negotiations, and emotion-aware mobile applications
Detection of Respiratory Arousals Using Photoplethysmography (PPG) Signal in Sleep Apnea Patients
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Cochlear Transform
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-08-2023
DOI: 10.36227/TECHRXIV.24010356.V2
Abstract: Progress in signal processing seems to converge towards perceptually inspired algorithms. This work proposes a novel signal processing framework, namely Cochlear Transform (CT), inspired by the active spiral cochlear mechanics. CT reflects how the spiral cochlea transforms the input stimuli to spatially supported and tonotopically organized basilar membrane responses, resulting in orthogonal cochlear modes with bio-inspired Time-Frequency Representation (TFR). We treat an input signal as a propagating wave that travels from the cochlear base to apex, along a path that takes a spiral shape from θ = 0◦ to θ = 990◦, respectively. More importantly, we showcase the generalizability of the CT to non-acoustic signals by modulating them to the highest sensitivity region of the cochlea.We try to achieve balance between bio-inspiration and adaptive signal processing. Accordingly, we demonstrate that CT attains important characteristics, such as filterbank with perceptual tuning when excited by White Gaussian Noise, noise robustness, and the cochlear decomposition orthogonality. Numerical ex les of synthetic and real signals are given to demonstrate the robustness of CT and comparisons are made with Empirical Wavelet Transform (EWT) and the Variational Mode Decomposition (VMD). CT reveals new unseen and interpretable features not only of acoustic signals, but also real-life biological and seismic signals that stem from the active cochlear mechanics including acoustic distortion effects. This study is the first to demonstrate that mimicking both the active function and the structure of the human cochlear results inenhanced and interpretable TFR resolution, which allows the generalization of the approach to non-acoustic signals. CT resembles spectro-temporal receptive fields necessary for downstream perceptual auditory tasks with a range of application in data analysis, enhancing synergies between machine intelligence and bio-inspired perception. It also paves the way for bio-inspired feature engineering methods, including future CT extensions to applications for multivariate signals and 2D image processing.
Cochlear Transform
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-08-2023
DOI: 10.36227/TECHRXIV.24010356
Abstract: Progress in signal processing seems to converge towards perceptually inspired algorithms. This work proposes a novel signal processing framework, namely Cochlear Transform (CT), inspired by the active spiral cochlear mechanics. CT reflects how the spiral cochlea transforms the input stimuli to spatially supported and tonotopically organized basilar membrane responses, resulting in orthogonal cochlear modes with bio-inspired Time-Frequency Representation (TFR). We treat an input signal as a propagating wave that travels from the cochlear base to apex, along a path that takes a spiral shape from θ = 0◦ to θ = 990◦, respectively. More importantly, we showcase the generalizability of the CT to non-acoustic signals by modulating them to the highest sensitivity region of the cochlea.We try to achieve balance between bio-inspiration and adaptive signal processing. Accordingly, we demonstrate that CT attains important characteristics, such as filterbank with perceptual tuning when excited by White Gaussian Noise, noise robustness, and the cochlear decomposition orthogonality. Numerical ex les of synthetic and real signals are given to demonstrate the robustness of CT and comparisons are made with Empirical Wavelet Transform (EWT) and the Variational Mode Decomposition (VMD). CT reveals new unseen and interpretable features not only of acoustic signals, but also real-life biological and seismic signals that stem from the active cochlear mechanics including acoustic distortion effects. This study is the first to demonstrate that mimicking both the active function and the structure of the human cochlear results inenhanced and interpretable TFR resolution, which allows the generalization of the approach to non-acoustic signals. CT resembles spectro-temporal receptive fields necessary for downstream perceptual auditory tasks with a range of application in data analysis, enhancing synergies between machine intelligence and bio-inspired perception. It also paves the way for bio-inspired feature engineering methods, including future CT extensions to applications for multivariate signals and 2D image processing.
Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management
Publisher: JMIR Publications Inc.
Date: 21-06-2022
DOI: 10.2196/38614
Abstract: Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various “smart” mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of in idual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.
Regulation of minimum toe clearance variability in the young and elderly during walking on sloped surfaces
Publisher: IEEE
Date: 08-2007
Feasibility of Non-invasive Fetal Electrocardiographic Interval Measurement in the Outpatient Clinical Setting.
Publisher: Springer Science and Business Media LLC
Date: 06-06-2019
DOI: 10.1007/S00246-019-02128-W
Abstract: Non-invasive fetal electrocardiography (ECG) is a promising method for evaluating fetal cardiac electrical activity. Despite advances in fetal ECG technology, its ability to provide reliable, interpretable results in a typical outpatient fetal cardiology setting remains unclear. We sought to determine the feasibility of measuring standard ECG intervals in an outpatient fetal cardiology practice using an abdominal fetal ECG device that employs blind source separation with reference, an innovative signal-processing technique for fetal ECG extraction. Women scheduled for clinically indicated outpatient fetal echocardiogram underwent 10 min of fetal ECG acquisition from the maternal abdomen using specialized gel electrodes. A bedside laptop computer performed fetal ECG extraction, allowing real-time visualization of fetal and maternal ECG signals. Offline post-processing of 1 min of recorded data yielded fetal P-wave duration, PR interval, QRS duration, RR interval, QT interval, and QTc. Fifty-five fetuses were studied with gestational age 18-37 weeks, including 13 with abnormal fetal echocardiogram findings and three sets of twins. Interpretable results were obtained in 91% of fetuses, including 85% during the vernix period and 100% of twin fetuses. PR interval and RR interval of 18-24 week gestation fetuses were significantly shorter than those with gestational age 25-31 and 32-37 weeks. Of the six fetuses with abnormal rhythms on fetal echocardiogram, fetal ECG tracing was interpretable in five and matched the rhythm noted on fetal echocardiogram. Abdominal fetal ECG acquisition is feasible for arrhythmia detection and ECG interval calculation in a routine clinical setting.
Advanced Poincaré plot analysis differentiates between hypertensive pregnancy disorders
Publisher: IOP Publishing
Date: 06-09-2011
DOI: 10.1088/0967-3334/32/10/009
Abstract: Hypertensive pregnancy disorders affect 6% to 8% of all pregnancies and can result in severe complications for the mother and the foetus of which pre-ecl sia (PE) has the worst perinatal outcome. Several studies suggested that the autonomic nervous system plays an important role in the process of developing hypertensive pregnancy disorders, especially PE. The aim of this retrospective study was to investigate whether women with PE could be differentiated from women with various other hypertensive pregnancy disorders, by employing an enhanced Poincaré plot analysis (PPA), the segmented Poincaré plot analysis (SPPA), to their beat-to-beat interval and blood pressure signals. Sixty-nine pregnant women with hypertensive disorders (29 PE, 40 with chronic or gestational hypertension) were included. The SPPA as well as the traditional PPA found significant differences between PE and other hypertensive disorders of diastolic blood pressure (p < 0.001 versus p < 0.001) but only the SPPA method revealed significant differences (p < 0.001) also of the systolic blood pressure. Further on, linear discrimination analysis demonstrated that indices derived from SPPA are more suitable for differentiation between chronic and gestational hypertension and PE than those from traditional PPA (area under the ROC curve 0.85 versus 0.69). Therefore this procedure could contribute to the differential diagnosis of hypertensive pregnancy disorders.
Heart rate variability and complexity in people with diabetes associated cardiac autonomic neuropathy
Publisher: IEEE
Date: 08-2008
Genetics of diabetic kidney disease: A follow‐up study in the Arab population of the United Arab Emirates
Publisher: Wiley
Date: 30-09-2019
DOI: 10.1002/MGG3.985
Abstract: Two genome‐wide association studies in European and Japanese populations reported on new loci for diabetic kidney disease (DKD), including FTO . In this study, we have replicated these investigations on a cohort of 410 Type 2 diabetes mellitus (T2DM) patients of Arab origin from the United Arab Emirates (UAE). The cohort included 145 diabetic patients diagnosed with DKD and 265 diabetics free of the disease. In general, we were able to confirm the association between the FTO locus and DKD, as reported in the Japanese population. Specifically, there were significant associations with two single nucleotide polymorphisms (SNPs), namely rs1421086 ( p = .013, OR = 1.52 depending on allele G, 95% CI: 1.09–2.11) and rs17817449 ( p = .0088, OR = 1.55 depending on allele C, 95% CI: 1.12–2.14) of the FTO locus. Both SNPs were in linkage disequilibrium with rs56094641, also as reported in the Japanese population. While the alleles of both SNPs, which increase the risk of DKD, were associated with higher Body Mass Index (BMI), their associations with DKD were independent of the BMI effects. This study confirms that FTO is a multiethnic locus for DKD which is independent from any influence of BMI and/or obesity.
Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes
Publisher: IEEE
Date: 08-2015
Application of automated fetal valve motion identification to investigate fetal heart anomalies
Publisher: IEEE
Date: 10-2015
LSTM-Modeling of Emotion Recognition Using Peripheral Physiological Signals in Naturalistic Conversations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Simulink-based sleep apnea screening model for portable diagnosis
Publisher: IEEE
Date: 12-2009
Related Organisations
Bangladesh University Of Engineering And Technology
Location: Bangladesh
Khalifa University Of Science And Technology
Location: United Arab Emirates
Related Funding Activities
A Low-Cost Phonogram Device For Screening Fetal Wellbeing
Start Date: 2012
End Date: 2014
Funder: Bill and Melinda Gates Foundation
View Funded ActivityLinkage Projects - Grant ID: LP100200184
Start Date: 06-2012
End Date: 06-2015
Amount: $240,000.00
Funder: Australian Research Council
View Funded Activity