Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images

BACKGROUND AND OBJECTIVE

Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information.

METHODS

In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information.

RESULTS

Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage.

CONCLUSIONS

The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms

One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low- and middle-income countries. Here, we developed a deep learning-based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23 % and 72.41 %, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10 % when evaluated on unseen data. The model had accuracies of 70 %, 88 %, and 86 % in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment.

Investigation on explainable machine learning models to predict chronic kidney diseases

Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world's population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflicted. In this work, we investigate the use of an explainable artificial intelligence (XAI)-based strategy, leveraging clinical characteristics, to predict CKD. This study collected clinical data from 491 patients, comprising 56 with CKD and 435 without CKD, encompassing clinical, laboratory, and demographic variables. To develop the predictive model, five machine learning (ML) methods, namely logistic regression (LR), random forest (RF), decision tree (DT), Naïve Bayes (NB), and extreme gradient boosting (XGBoost), were employed. The optimal model was selected based on accuracy and area under the curve (AUC). Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) algorithms were utilized to demonstrate the influence of the features on the optimal model. Among the five models developed, the XGBoost model achieved the best performance with an AUC of 0.9689 and an accuracy of 93.29%. The analysis of feature importance revealed that creatinine, glycosylated hemoglobin type A1C (HgbA1C), and age were the three most influential features in the XGBoost model. The SHAP force analysis further illustrated the model's visualization of individualized CKD predictions. For further insights into individual predictions, we also utilized the LIME algorithm. This study presents an interpretable ML-based approach for the early prediction of CKD. The SHAP and LIME methods enhance the interpretability of ML models and help clinicians better understand the rationale behind the predicted outcomes more effectively.

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient's cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

A machine learning driven monogram for predicting chronic kidney disease stages 3-5

Chronic kidney disease (CKD) remains one of the most prominent global causes of mortality worldwide, necessitating accurate prediction models for early detection and prevention. In recent years, machine learning (ML) techniques have exhibited promising outcomes across various medical applications. This study introduces a novel ML-driven monogram approach for early identification of individuals at risk for developing CKD stages 3-5. This retrospective study employed a comprehensive dataset comprised of clinical and laboratory variables from a large cohort of diagnosed CKD patients. Advanced ML algorithms, including feature selection and regression models, were applied to build a predictive model. Among 467 participants, 11.56% developed CKD stages 3-5 over a 9-year follow-up. Several factors, such as age, gender, medical history, and laboratory results, independently exhibited significant associations with CKD (p < 0.05) and were utilized to create a risk function. The Linear regression (LR)-based model achieved an impressive R-score (coefficient of determination) of 0.954079, while the support vector machine (SVM) achieved a slightly lower value. An LR-based monogram was developed to facilitate the process of risk identification and management. The ML-driven nomogram demonstrated superior performance when compared to traditional prediction models, showcasing its potential as a valuable clinical tool for the early detection and prevention of CKD. Further studies should focus on refining the model and validating its performance in diverse populations.

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review

The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches' efficacy.

Effect of depression on phase coherence between respiratory sinus arrhythmia and respiration during sleep in patients with obstructive sleep apnea

Introduction: A high prevalence of major depressive disorder (MDD) among Obstructive Sleep Apnea (OSA) patients has been observed in both community and clinical populations. Due to the overlapping symptoms between both disorders, depression is usually misdiagnosed when correlated with OSA. Phase coherence between respiratory sinus arrhythmia (RSA) and respiration (λ RSA-RESP) has been proposed as an alternative measure for assessing vagal activity. Therefore, this study aims to investigate if there is any difference in λ RSA-RESP in OSA patients with and without MDD. Methods: Electrocardiograms (ECG) and breathing signals using overnight polysomnography were collected from 40 OSA subjects with MDD (OSAD+), 40 OSA subjects without MDD (OSAD-), and 38 control subjects (Controls) without MDD and OSA. The interbeat intervals (RRI) and respiratory movement were extracted from 5-min segments of ECG signals with a single apneic event during non-rapid eye movement (NREM) [353 segments] and rapid eye movement (REM) sleep stages [298 segments]. RR intervals (RRI) and respiration were resampled at 10 Hz, and the band passed filtered (0.10-0.4 Hz) before the Hilbert transform was used to extract instantaneous phases of the RSA and respiration. Subsequently, the λ RSA-RESP between RSA and Respiration and Heart Rate Variability (HRV) features were computed. Results: Our results showed that λ RSA-RESP was significantly increased in the OSAD+ group compared to OSAD- group during NREM and REM sleep. This increase was accompanied by a decrease in the low frequency (LF) component of HRV. Discussion: We report that the phase synchronization index between RSA and respiratory movement could provide a useful measure for evaluating depression in OSA patients. Our findings suggest that depression has lowered sympathetic activity when accompanied by OSA, allowing for stronger synchronization between RSA and respiration.

Multiscale Diffusion Entropy Analysis for the Detection of Crucial Events in Cardiac Pathology

The significance of crucial events in explaining the dynamics of a physiological system has only been recently emerging. Crucial events are yet to be fully understood and implemented in clinical applications of physiological signal processing. This paper proposes the application of modified diffusion entropy (MDEA) and novel multiscale diffusion entropy analyses (MSDEA) on measuring the temporal complexity of the ECG time series to improve crucial events detection performance. Thirty samples of each of three groups of ECG datasets from PhysioNet with recordings of cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythm (NSR) were analyzed using MDEA with stripes followed by MSDEA. Healthy NSR ECGs showed an approximate 15% greater inverse power law (IPL) and scaling δ indices than pathologic CHF and ARR signals. Additionally, the scaling indices for the pathologic groups showed higher standard deviations, indicating that crucial events determined by MDEA reveal latent differences in ECG complexity that could better be investigated across multiple time scales of temporally decomposed signals using MSDEA which combines multiscale entropy (MSE) and MDEA. Hence, MSDEA showed an improved, clearer discrimination between the healthy and pathological cardiac signals (p<0.0005) characterized by a range of NSR complexity indices twice the range of the pathological values associated with ARR and CHF across twenty temporal scales as well as more reliable trend lines (R2>=0.95).Clinical Relevance- This research proposes a novel and enhanced diagnostic discrimination across healthy and pathologic cardiac conditions based on biomedical signal processing of ECG recordings utilizing the principle of crucial events detection.

A novel model for predicting hospitalization risk among hemodialysis patients based on blood test variables

Hemodialysis patients are at high risk of hospitalization. Predicting such risk in dialysis patients may be critical to maintaining quality of life and reducing costs to the healthcare system. In this paper, we present and fractional polynomial stepwise logistic regression model to specify how routinely collected blood test variables could be linked to a significant increase in hospitalization risk. We found that eight of nineteen variables were significantly able to predict hospitalization risk; albumin (p<0.05), creatinine (p<0.05), calcium (p<0.01), bicarbonate (p<0.01), hemoglobin (p<0.05), mean cell hemoglobin concentration (MCHC) (p<0.0001), mean corpuscular volume (MCV) (p<0.0001), and potassium (p<0.01). The model achieved accuracy, sensitivity, and specificity of 77.31%, 83.03%, and 69.05%, respectively.

Recognition of Pediatric Congenital Heart Diseases by Using Phonocardiogram Signals and Transformer-Based Neural Networks

The phonocardiogram (PCG) or heart sound auscultation is a low-cost and non-invasive method to diagnose Congenital Heart Disease (CHD). However, recognizing CHD in the pediatric population based on heart sounds is difficult because it requires high medical training and skills. Also, the dependency of PCG signal quality on sensor location and developing heart in children are challenging. This study proposed a deep learning model that classifies unprocessed or raw PCG signals to diagnose CHD using a one-dimensional Convolution Neural Network (1D-CNN) with an attention transformer. The model was built on the raw PCG data of 484 patients. The results showed that the attention transformer model had a good balance of accuracy of 0.923, a sensitivity of 0.973, and a specificity of 0.833. The Receiver Operating Characteristic (ROC) plot generated an Area Under Curve (AUC) value of 0.964, and the F1-score was 0.939. The suggested model could provide quick and appropriate real-time remote diagnosis application in classifying PCG of CHD from non-CHD subjects.Clinical Relevance- The suggested methodology can be utilized to analyze PCG signals more quickly and affordably for rural doctors as a first screening tool before sending the cases to experts.

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning. Annu Int Conf IEEE Eng Med Biol Soc 2023;2023:1-4. [PMID: 38083567 DOI: 10.1109/embc40787.2023.10341132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Heart failure refers to the inability of the heart to pump enough amount of blood to the body. Nearly 7 million people die every year because of its complications. Current gold-standard screening techniques through echocardiography do not incorporate information about the circadian rhythm of the heart and clinical information of patients. In this vein, we propose a novel approach to integrate 24-hour heart rate variability (HRV) features and patient profile information in a single multi-parameter and color-coded polar representation. The proposed approach was validated by training a deep learning model from 7,575 generated images to predict heart failure groups, i.e., preserved, mid-range, and reduced left ventricular ejection fraction. The developed model had overall accuracy, sensitivity, and specificity of 93%, 88%, and 95%, respectively. Moreover, it had a high area under the receiver operating characteristics curve (AUROC) of 0.88 and an area under the precision-recalled curve (AUPR) of 0.79. The novel approach proposed in this study suggests a new protocol for assessing cardiovascular diseases to act as a complementary tool to echocardiography as it provides insights on the circadian rhythm of the heart and can be potentially personalized according to patient clinical profile information.Clinical relevance- Implementing polar representations with deep learning in clinical settings to supplement echocardiography leverages continuous monitoring of the heart's circadian rhythm and personalized cardiovascular medicine while reducing the burden on medical practitioners.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

INTRODUCTION

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

AREAS COVERED

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

EXPERT OPINION

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

EEG-based Emotion Recognition Using Sub-Band Time-Delay Correlations

Emotion recognition is a challenging task with many potential applications in psychology, psychiatry, and human-computer interaction (HCI). The use of time-delay information in the controlled time-delay stability (cTDS) algorithm can help to capture the temporal dynamics of EEG signals, including sub-band information and bi-directional coupling that can aid in emotion recognition and identification of specific connectivity patterns between brain rhythms. Incorporating EEG frequency bands can be used to design better emotion recognition systems. This paper evaluates the cTDS algorithm for binary classification tasks of arousal and valence using EEG sub-band signals. This method achieved a high accuracy of 91.1% for arousal and 91.7% for valence based on one electrode recording site at Fp1. The cTDS algorithm is a promising approach to analyzing brain network interactions. It can be particularly applicable to arousal and valence classification tasks, especially within a complex, multimodal feature space associated with understanding psychiatric disorders and HCI applications.

Parkinsonian Tremor Detection with Compact Convolutional Transformer from Bispectrum Representation of tri-Axial Accelerometer Signals

After the breakthroughs of Transformer networks in Natural Language Processing (NLP) tasks, they have led to exciting progress in visual tasks as well. Nonetheless, there has been a parallel growth in the number of parameters and the amount of training data, which led to the conclusion that Transformers are not suited for small datasets. This paper is the first to convey the feasibility of Compact Convolutional Transformers (CCT) for the prediction of Parkinsonian postural tremor based on the Bispectrum (BS) representation of IMU accelerometer time series. The dataset includes tri-axial accelerometer signals collected unobtrusively in-the-wild while subjects are on a phone call, and labelled by neurologists and signal processing experts. The BS is a noise-immune, higher-order representation that reflects a signal's deviation from Gaussianity and measures quadratic phase coupling. We performed comparative classification experiments using the CCT, pre-trained CNNs such as VGG-16 and ResNet-50, and the conventional Vision Transformer (ViT). Our model achieves competitive prediction accuracy and F1 score of 96% with only 1.016 M trainable parameters, compared to the ViT with 21.659 M trainable parameters, in a five-fold cross-validation scheme. Our model also outperforms pre-trained CNNs such as VGG-16 and ResNet-50. Furthermore, we show that the performance gains are maintained when training on a larger dataset of BS images. Our effort here is motivated by the hypothesis that data-efficient transformers outperform transfer learning using pre-trained CNNs, paving the way for promising deep learning architecture for small-scale, novel and noisy medical imaging datasets.Clinical relevance- Novel deep learning model for unobtrusive prediction of Parkinsonian Postural Tremor from Bispectrum image representation of tri-axial accelerometer signals collected in-the-wild.

Inflammation, oxidative stress and mitochondrial dysfunction in the progression of type II diabetes mellitus with coexisting hypertension

Introduction

Type II diabetes mellitus (T2DM) is a metabolic disorder that poses a serious health concern worldwide due to its rising prevalence. Hypertension (HT) is a frequent comorbidity of T2DM, with the co-occurrence of both conditions increasing the risk of diabetes-associated complications. Inflammation and oxidative stress (OS) have been identified as leading factors in the development and progression of both T2DM and HT. However, OS and inflammation processes associated with these two comorbidities are not fully understood. This study aimed to explore changes in the levels of plasma and urinary inflammatory and OS biomarkers, along with mitochondrial OS biomarkers connected to mitochondrial dysfunction (MitD). These markers may provide a more comprehensive perspective associated with disease progression from no diabetes, and prediabetes, to T2DM coexisting with HT in a cohort of patients attending a diabetes health clinic in Australia.

Methods

Three-hundred and eighty-four participants were divided into four groups according to disease status: 210 healthy controls, 55 prediabetic patients, 32 T2DM, and 87 patients with T2DM and HT (T2DM+HT). Kruskal-Wallis and χ2 tests were conducted between the four groups to detect significant differences for numerical and categorical variables, respectively.

Results and discussion

For the transition from prediabetes to T2DM, interleukin-10 (IL-10), C-reactive protein (CRP), 8-hydroxy-2'-deoxyguanosine (8-OHdG), humanin (HN), and p66^Shc were the most discriminatory biomarkers, generally displaying elevated levels of inflammation and OS in T2DM, in addition to disrupted mitochondrial function as revealed by p66^Shc and HN. Disease progression from T2DM to T2DM+HT indicated lower levels of inflammation and OS as revealed through IL-10, interleukin-6 (IL-6), interleukin-1β (IL-1β), 8-OHdG and oxidized glutathione (GSSG) levels, most likely due to antihypertensive medication use in the T2DM +HT patient group. The results also indicated better mitochondrial function in this group as shown through higher HN and lower p66^Shc levels, which can also be attributed to medication use. However, monocyte chemoattractant protein-1 (MCP-1) levels appeared to be independent of medication, providing an effective biomarker even in the presence of medication use. The results of this study suggest that a more comprehensive review of inflammation and OS biomarkers is more effective in discriminating between the stages of T2DM progression in the presence or absence of HT. Our results further indicate the usefulness of medication use, especially with respect to the known involvement of inflammation and OS in disease progression, highlighting specific biomarkers during disease progression and therefore allowing a more targeted individualized treatment plan.

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction

Heart failure is characterized by sympathetic activation and parasympathetic withdrawal leading to an abnormal autonomic modulation. Beta-blockers (BB) inhibit overstimulation of the sympathetic system and are indicated in heart failure patients with reduced ejection fraction. However, the effect of beta-blocker therapy on heart failure with preserved ejection fraction (HFpEF) is unclear. ECGs of 73 patients with HFpEF > 55% were recruited. There were 56 patients in the BB group and 17 patients in the without BB (NBB) group. The HRV analysis was performed for the 24-h period using a window size of 1,4 and 8-h. HRV measures between day and night for both the groups were also compared. Percentage change in the BB group relative to the NBB group was used as a measure of difference. RMSSD (13.27%), pNN50 (2.44%), HF power (44.25%) and LF power (13.53%) showed an increase in the BB group relative to the NBB group during the day and were statistically significant between the two groups for periods associated with high cardiac risk during the morning hours. LF:HF ratio showed a decrease of 3.59% during the day. The relative increase in vagal modulated RMSSD, pNN50 and HF power with a decrease in LF:HF ratio show an improvement in the parasympathetic tone and an overall decreased risk of a cardiac event especially during the morning hours that is characterized by a sympathetic surge.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

The gold-standard treatment for social anxiety disorder: A roadmap for the future

Exposure therapy (ET), which follows the Pavlovian extinction model, is regarded as the gold-standard treatment for social anxiety disorder (SAD). The prospect of virtual reality in lieu of a traditional laboratory setting for the treatment of SAD has not been rigorously explored. The aim of the review was to summarize, find gaps in the current literature, and formulate future research direction by identifying two broad research questions: the comparative efficacy between in vivo ET and virtual reality exposure therapy (VRET) and the effectiveness of the Pavlovian extinction model in treating SAD. The criteria for effectiveness were effect size, relapse prevention, attrition rate and ecological validity. A literature search on recent randomized controlled trials yielded a total of 6 original studies (N=358), excluding duplication and overlapping participants. All studies supported that VRET was as effective as in vivo ET. Behavioral therapy that follows classical conditioning principles has a high attrition and relapse rate. Comparisons were drawn between the efficacy of the Pavlovian extinction model and other existing models, including third-wave approaches. The neural markers are suggested to be included as efficacy measures in treating SAD. The gold-standard treatment for SAD requires a paradigm shift through rigorous longitudinal comparative studies.

Deep learning identifies cardiac coupling between mother and fetus during gestation

In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20–40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices.

Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis

The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.

Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation

Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.

Investigating the effect of cholinergic and adrenergic blocking agents on maternal-fetal heart rates and their interactions in mice fetuses

This study examines the role of autonomic control of maternal and fetal heart rate variability (MHRV and FHRV) and their heartbeats phase coupling prevalence (CP_heartbeat) in mice. The subjects are divided 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 CP_heartbeat. 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 CP_heartbeat 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.

Summary: Autonomic development of fetal mice is analyzed through electrocardiography. Saline infusion does not alter maternal and fetal heart rate variation and coupling significantly. Atropine increases maternal heart rate, while propranolol lowers fetal heart rate.

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles

Microvascular complications are one of the key causes of mortality among type 2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications using only the patient demographic, clinical, and laboratory profiles. A total of 96 Bangladeshi participants with type 2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a chi-squared (χ²) test to statistically determine the most important markers in predicting three microvascular complications: cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (RET). A machine learning approach based on logistic regression, random forest (RF), and support vector machine (SVM) algorithms was then developed to ensure automated clinical testing for microvascular complications in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, albumin–creatinine ratio, and gender for CAN testing (98.67%); microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%); and hemoglobin A1C, microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prevent those patients from further microvascular complications leading to early death.

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

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 that 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). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The 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, p-value: <0.001). 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.

Genetic Variants and Their Associations to Type 2 Diabetes Mellitus Complications in the United Arab Emirates

Aim

Type 2 Diabetes Mellitus (T2DM) is associated with microvascular complications, including diabetic retinopathy (DR), diabetic nephropathy (DNp), and diabetic peripheral neuropathy (DPN). In this study, we investigated genetic variations and Single Nucleotide Polymorphisms (SNPs) associated with DR, DNp, DPN and their combinations among T2DM patients of Arab origin from the United Arab Emirates, to establish the role of genes in the progression of microvascular diabetes complications.

Methods

A total of 158 Emirati patients with T2DM were recruited in this study. The study population was divided into 8 groups based on the presence of single, dual, or all three complications. SNPs were selected for association analyses through a search of publicly available databases, specifically genome-wide association study (GWAS) catalog, infinome genome interpretation platform, and GWAS Central database. A multivariate logistic regression analysis and association test were performed to evaluate the association between 83 SNPs and DR, DNp, DPN, and their combinations.

Results

Eighty-three SNPs were identified as being associated with T2DM and 18 SNPs had significant associations to one or more diabetes complications. The most strongly significant association for DR was rs3024997 SNP in the VEGFA gene. The top-ranked SNP for DPN was rs4496877 in the NOS3 gene. A trend towards association was detected at rs833068 and rs3024998 in the VEGFA gene with DR and rs743507 and rs1808593 in the NOS3 gene with DNp. For dual complications, the rs833061, rs833068 and rs3024997 in the VEGFA gene and the rs4149263 SNP in the ABCA1 gene were also with borderline association with DR/DNp and DPN/DNp, respectively. Diabetic with all of the complications was significantly associated with rs2230806 in the ABCA1 gene. In addition, the highly associated SNPs rs3024997 of the VEGFA gene and rs4496877 of the NOS3 gene were linked to DR and DPN after adjusting for the effects of other associated markers, respectively.

Conclusions

The present study reports associations of different genetic polymorphisms with microvascular complications and their combinations in Emirati T2DM patients, reporting new associations, and corroborating previous findings. Of interest is that some SNPs/genes were only present if multiple comorbidities were present and not associated with any single complication.

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.

A Comparative Study of Arousal and Valence Dimensional Variations for Emotion Recognition Using Peripheral Physiological Signals Acquired from Wearable Sensors. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:1104-1107. [PMID: 34891480 DOI: 10.1109/embc46164.2021.9630759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Wearable sensors have made an impact on healthcare and medicine by enabling out-of-clinic health monitoring and prediction of pathological events. Further advancements made in the analysis of multimodal signals have been in emotion recognition which utilizes peripheral physiological signals captured by sensors in wearable devices. There is no universally accepted emotion model, though multidimensional methods are often used, the most popular of which is the two-dimensional Russell's model based on arousal and valence. Arousal and valence values are discrete, usually being either binary with low and high labels along each dimension creating four quadrants or 3-valued with low, neutral, and high labels. In day-to-day life, the neutral emotion class is the most dominant leaving emotion datasets with the inherent problem of class imbalance. In this study, we show how the choice of values in the two-dimensional model affects the emotion recognition using multiple machine learning algorithms. Binary classification resulted in an accuracy of 87.2% for arousal and up to 89.5% for valence. Maximal 3-class classification accuracy was 80.9% for arousal and 81.1% for valence. For the joined classification of arousal and valence, the four-quadrant model reached 87.8%, while the nine-class model had an accuracy of 75.8%. This study can be used as a basis for further research into feature extraction for better overall classification performance.

Swarm Decomposition of Abdominal Signals for Non-invasive Fetal ECG Extraction

The non-invasive fetal electrocardiography (fECG) extraction from maternal abdominal signals is one of the most promising modern fetal monitoring techniques. However, the noninvasive fECG signal is heavily contaminated with noise and overlaps with other prominent signals like the maternal ECG. In this work we propose a novel approach in non-invasive fECG extraction using the swarm decomposition (SWD) to isolate the fetal components from the abdominal signal. Accompanied with the use of higher-order statistics (HOS) for R peak detection, the application of the proposed method to the Abdominal and Direct Fetal ECG PhysioNet Database resulted in fetal R peak detection sensitivity of 99.8% and a positive predictability of 99.8%. Our results demonstrate the applicability of SWD and its potentiality in extracting fECG of good morphological quality with more deep decomposition levels, in order to connect the extracted structural characteristics of the fECG with the health status of the fetus.Clinical Relevance- The developed method shows improvement in fetal R peak detection for certain signals.

Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models

OBJECTIVES

The purpose of this study was to set an optimal fit of the estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines.

METHODS

Support vector regression (SVR) models were applied to estimate LVEF from ECG derived heart rate variability (HRV) data in one-hour intervals from 24-hour ECG recordings of patients with either preserved, mid-range, or reduced LVEF, obtained from the Intercity Digital ECG Alliance (IDEAL) study. A step-wise feature selection approach was used to ensure the best possible estimations of LVEF levels.

RESULTS

The experimental results have shown that the lowest Root Mean Square Error (RMSE) between the original and estimated LVEF levels was during 3-4 am, 5-6 am and 6-7 pm.

CONCLUSION

The observations suggest these hours as possible times for intervention and optimal treatment outcomes. In addition, LVEF classifications following the ACCF/AHA guidelines leads to a more accurate assessment of mid-range LVEF.

SIGNIFICANCE

This study paves the way to explore the use of HRV features in the prediction of LVEF percentages as an indicator of disease progression, which may lead to an automated classification process for CAD patients.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Investigating myocardial performance in normal and sick fetuses by abdominal Doppler signal derived indices

INTRODUCTION

Fetal myocardial performance indices are applied to assess aspects of systolic and diastolic function in developing fetal heart. The aim of this study was to determine normal values of Tei Index (TI) and modified TI (KI) for systolic and diastolic performance in early (<30 weeks), Mid (30-35 weeks) and late (36-41 weeks) relating to both normal fetuses as well as fetuses carrying a variety of fetal abnormalities, which do not call for precise anatomic imaging.

MATERIAL AND METHODS

Fetal Electrocardiogram Signals (FES) and Doppler Ultrasound Signal (DUS) were simultaneously documented from 55 normal and 25 abnormal fetuses with a variety of abnormalities including Congenital Heart Diseases (CHDs) and a variety of non-CHDs. The isovolumic contraction time (ICT), isovolumic relaxation time (IRT), ventricular ejection time (VET) and ventricular filling time (VFT) were estimated from continuous DUS signals by a hybrid of Hidden Markov and Support Vector Machine based automated model. The TI and the KI were calculated by using the formula (ICT ​+ ​IRT)/VET and (ICT ​+ ​IRT)/VFT respectively.

RESULTS

The TI was not found to show any significant change from early to late fetuses, nor between normal and abnormal cases. On the other hand, KI was shown to significantly decline in values from early to late normal cases and from normal to abnormal groups. Significant correlation (r = -0.36; p < 0.01) of gestational ages with only KI (not TI) was found in this study.

CONCLUSION

Modified TI (KI) may be a useful index to monitor the normal development of fetal myocardial function and identify fetuses with a variety of CHD and non-CHD cases.

Estimating Fetal Age by Fetal Maternal Heart Rate Coupling Parameters

Beat-by-beat maternal and fetal heart couplings were reported to be evident throughout the fetal development. However, it is still unknown whether maternal-fetal heartbeat coupling parameters are associated with fetal development, and the potential interrelationships. Therefore, this study aims to investigate the associations of coupling parameters with fetal gestational age by multivariate regression models. Ten min abdominal lead-based maternal and fetal ECG signals were collected from 16 healthy pregnant women with healthy singleton pregnancies (19-32 weeks). Maternal and Fetal Heart Rate Variability (MHRV and FHRV) values as well as maternal-fetal heart rate coupling (strength, measured by A) parameters at various coupling ratios (associated with different Maternal:Fetal heartbeat ratios of 1:2, 1:3, 2:3, 2:4, 3:4, and 3:5) were calculated. Based on those features stepwise multivariate regression models were constructed by validating against the gold standard gestational age identified by crown-rump length from doppler echocardiogram. Among all models, the best model (Root Mean Square Error, RMSE=1.92) was found to be significantly (p<0.05) associated with mean fetal heart rate, mean maternal heart rate, standard deviation of maternal heart rate, λ[1:3], λ[2:3], λ[2:4]. Correlation coefficients and Bland Altman plots were constructed to statistically validate the results. The model developed based on coupling parameters only, showed the second-best performance (RMSE=2.50). Therefore, combining maternal and fetal heart rate variability parameters with maternal-fetal heart rate coupling values (rather than considering FHRV or MHRV parameters only) is found to be better associated with fetal development.Clinical relevance- This is a brief additional statement on why this might be of interest to practicing clinicians. Example: This establishes the anesthetic efficacy of 10% intraosseous injections with epinephrine to positively influence cardiovascular function.

Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.

Fetal Cardiac Timing Events Estimation from Doppler Ultrasound Signal Cepstrum Analysis

Early diagnosis of the cardiac abnormalities during the pregnancy may reduce the risk of perinatal morbidity and mortality. Doppler ultrasound signals (DUS), which is commonly used for monitoring the fetal heart rate, can also be used for identifying the event timings of fetal cardiac valve motions. In this paper, we propose a non-invasive technique to identify the fetal cardiac timing events on the basis of analysis of fetal DUS (10 normal subjects and 6 abnormal subjects). We proposed using the cepstrum analysis which enabled the frequency contents of the Doppler signals to be linked to the opening and closing of the heart's valves (Aortic and mitral). The time intervals from R peak of fetal ECG to opening and closing of aortic valve were found to be 123.83±7.41 (msec) and 222.85±21.21 (msec), respectively. The time intervals from R peak to opening and closing of mitral valve were found to be 295.03±13.62 (msec) and 31.97±3.34 (msec), respectively. This feature is essential for diagnosing the disorder of cardiac rhythm, extremely used for diagnosis of heart abnormalities.

Effect of β-blocker on maternal-fetal heart rates and coupling in pregnant mice and fetuses

The aim of this preliminary study is to look how maternal-fetal heart rates and their coupling patterns are influenced by injection of β blocker(propranolol) into pregnant mice. Total of 6 pregnant female mice were divided into two groups [control (N=3) and β blockade (N=3)]. On 17.5-day mean heart rate of mothers and fetuses (MHR and FHR) were simultaneously measured for 20 minutes (10 minutes under normal condition and 10 minutes with saline (to control group) and propranolol (to the β blockade group) solution by using an invasive maternal and fetal electrocardiogram techniques with needle electrodes. Results show that FHR decreased and maternal-fetal heart rate coupling (λ) patterns changed with propranolol infusion (no change with saline). Statistical test showed that changes (increase/decrease from pre to post values) in mean, rmssd and power spectral density (PSD) (2~4 Hz)) of MHR, short term variability of FHR, PSD (0.0~1.0 Hz) of FHR and λ were found to be significantly associated with treatment types (saline to propranolol). The presented results and protocol allow for assessment of β adrenergic control of maternal and fetal heart, which will further enhance the value of the mouse as a model of heritable human pregnancy and hypertension.

Singular value decomposition entropy as a measure of ankle dynamics efficacy in a Y-balance test following supportive lower limb taping

Complexity versus regularity is an important component of appropriate joint position to retain balance but has not received much attention. The Singular value decomposition entropy (SvdEn) characterizes information content or regularity of a signal depending on the number of vectors attributed to the process. The current study aimed to investigate the effect of kinesiology tape compared to static strapping tape and no tape on ankle joint dynamics during the Y balance test. Forty-one participants (21 males; 20 females) aged between 18 and 34 years of age completed the Y-balance test with kinesiology tape, with strapping tape and without tape applied to the dominant leg. SvdEn was obtained from center of pressure values, as well as ankle and knee movement variability during the Y balance test. Center of pressure and knee joint dynamics did not change significantly between the two taped and no tape conditions during the YBT. Ankle joint SvdEn was significantly lower in the anterior-posterior (p<; .05) and superior-inferior (p<; .001) direction for both tape conditions compared to no tape. Greater regularity in the ankle joint dynamics indicates less vectors are required to describe the signal, which can be interpreted from a neurophysiological perspective as a decrease in feedforward and/or feedback input along the hierarchical sensorimotor processing pathway as an adjustment to taping and a possibly more reflex oriented response localised at the spinal cord level.

Genetics of diabetic kidney disease: A follow-up study in the Arab population of the United Arab Emirates

BACKGROUND

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).

METHODS AND RESULTS

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.

CONCLUSIONS

This study confirms that FTO is a multiethnic locus for DKD which is independent from any influence of BMI and/or obesity.

Quantification of maternal-fetal cardiac couplings in normal and abnormal pregnancies applying high resolution joint symbolic dynamics

Maternal psycho-physiological activities affect the fetal development and its heart rate variability. In this work, the short-term maternal-fetal cardiac couplings in normal and abnormal fetuses were investigated by using the high resolution joint symbolic dynamics method. The analysis was applied on maternal and fetal beat-to-beat intervals of 66 normal and 19 abnormal fetuses that includes different types of congenital heart defects, tachycardia, Atrioventricular block and other types of abnormalities. Results showed that the weak decrease in maternal beat-to-beat variations associated with the strong increase in fetal beat-to-beat variations was found to be significantly higher for the abnormal cases compared to normal cases despite the heterogeneity of abnormality and gestational age (abnormal: 0.032 ±0.013, normal: 0.014 ±0.007, p < 0.01). These differences could be interpreted as impairment in the autonomic nervous system in abnormal cases. The atrioventricular block cases showed a rise in the strong increase and decrease fetal beat-to-beat variations compared to the normal cases while the tachycardia cases showed a decay in these coupling patterns.

Regulation of maternal-fetal heart rates and coupling in mice fetuses

The aim of this preliminary study is to investigate if there is any evidence of maternal-fetal heart rate coupling in mice fetuses and how the coupling patterns are regulated by vagal nervous system on beat by beat. Total of 6 pregnant female mice were divided into two groups [control (N=3) and vagal blockade (N=3)]. On 17.5-day beat-to-beat heart rates of mothers and fetuses (MHR and FHR) were simultaneously measured for 20 minutes (10 minutes under normal condition and 10 minutes with saline (to control group) and atropine (to the vagal blockade group)) solution by using an invasive maternal and fetal electrocardiogram techniques with needle electrodes. Results show that occasional strong maternal-fetal heart rate coupling (strength was measured by $\lambda$) appeared and its patterns changed with atropine infusion (no change with saline). Additionally, fisher's exact test shows that changes (increase/decrease from pre to post injection values) in mean, rmssd and power spectral density (PSD) (2~4 Hz) of MHR, rmssd FHR and PSD (2~4 Hz) of${\lambda }$were found to be significantly (p<0.05) associated with treatment types (saline/ atropine). The presented results and protocol allow for the first time in the assessment of autonomic regulation of maternal and fetal heart and their interactions, which will further enhance the value of the mouse as a murine model of heritable human pregnancy and perinatal complications due to maternal conditions.

Preliminary Evaluation of Fetal Congenital Heart Defects Changes on Fetal-Maternal Heart Rate Coupling Strength

Monitoring fetal heart rate in an important aspect in evaluating fetal well being. Maternal-fetal interaction has shown evolution during fetal maturation. In this work, we studied maternal-fetal heart rate synchronization in early and late gestation fetuses. We also evaluated variations in the synchronization due to congenital heart defect (CHD). Maternal-fetal heart rate synchronization for 22 early gestation (Age < 32 weeks), $late gestation (Age >32 weeks) and 7 CHD fetuses (5 of them with gestational age < 32 weeks). The synchronization ratio between the mother and the fetus was more localized at certain fetus heart rate in the early gestation group while it was spreading over more fetal heart rate for the late group. For example, for maternal primary cycle of 3 beat- to-beat (m=3), the synchronization ratio of 5 fetus beats (n=5) contributed 60±30% of the whole coupling ratios for the early group while it contributed 30°30% for the late group (p< 0.01). On the other hand, the coupling ratio of m:n=3:7 contributed 4±17% of the early group and 13±24% for the late group (p< 0.05). The standard deviation of the phase coherence index $(\lambda_{-\mathrm{S}\mathrm{D}})$ for both the late and the CHD groups were significantly higher than the early group at different values. For example, $\lambda -\mathrm{S}\mathrm{D} was 0.006\pm 0.004$ for the early group while it was 0.009±0.008 for the late group (p< 0.01) and 0.01± 0.002 for the CHD group (p< 0.01) for m=3. The variation between the early and late normal groups might indicate a healthy development of the autonomic nervous system while the higher variation in the CHD group could be a good marker for impairment of the cardiac autonomic activity. Further coupling analysis with more abnormal cases is needed to verify these findings.

Feasibility of Non-invasive Fetal Electrocardiographic Interval Measurement in the Outpatient Clinical Setting

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.

The Effect of Ankle Support on Lower Limb Kinematics During the Y-Balance Test Using Non-linear Dynamic Measures

Background: According to dynamical systems theory, an increase in movement variability leads to greater adaptability, which may be related to the number of feedforward and feedback mechanisms associated with movement and postural control. Using Higuchi dimension (HDf) to measure complexity of the signal and Singular Value Decomposition Entropy (SvdEn) to measure the number of attributes required to describe the biosignal, the purpose of this study was to determine the effect of kinesiology and strapping tape on center of pressure dynamics, myoelectric muscle activity, and joint angle during the Y balance test.

Method: Forty-one participants between 18 and 34 years of age completed five trials of the Y balance test without tape, with strapping tape (ST), and with kinesiology tape (KT) in a cross-sectional study. The mean and standard errors were calculated for the center of pressure, joint angles, and muscle activities with no tape, ST, and KT. The results were analyzed with a repeated measures ANOVA model (P_A < 0.05) fit and followed by Tukey post hoc analysis from the R package with probability set at P < 0.05.

Results: SvdEn indicated significantly decreased complexity in the anterior-posterior (p < 0.05) and internal-external rotation (p < 0.001) direction of the ankle, whilst HDf for both ST and KT identified a significant increase in ankle dynamics when compared to no tape (p < 0.0001) in the mediolateral direction. Taping also resulted in a significant difference in gastrocnemius muscle myoelectric muscle activity between ST and KT (p = 0.047).

Conclusion: Complexity of ankle joint dynamics increased in the sagittal plane of movement with no significant changes in the possible number of physiological attributes. In contrast, the number of possible physiological attributes contributing to ankle movement was significantly lower in the frontal and transverse planes. Simply adhering tape to the skin is sufficient to influence neurological control and adaptability of movement. In addition, adaptation of ankle joint dynamics to retain postural stability during a Y Balance test is achieved differently depending on the direction of movement.

Fetal Cardiac Timing Events Estimation From Doppler Ultrasound Signals Using Swarm Decomposition

Perinatal morbidity and mortality can be reduced when any cardiac abnormalities during a pregnancy are diagnosed early. Doppler Ultrasound Signals (DUS) are often used to monitor the heart rate of a fetus and they can also be used to identify the timing events of fetal cardiac valve motions. This paper proposed a novel, non-invasive technique which can be used to identify the fetal cardiac timing events based upon the analysis of fetal DUS (based upon 66 normal subjects belonging to three differing age groups) which can later be used to estimate fetal cardiac intervals from a DUS signal. The foundation of this method is a novel decomposition method referred to as Swarm Decomposition (SWD) which makes it possible for the frequency contents of Doppler signals to be associated with cardiac valve motions. These motions include the opening (o) and closing (c) of Aortic (A) and Mitral (M) valves. When compared the SWD method results to the Empirical Mode Decomposition for the validation, the fetal cardiac timings were estimated successfully when isolating the constituent parts of analyzed DUS signals with reduced complexity compared to EMD method. Pulsed Doppler images are used in order to verify the estimated timings. Three fetal age groups were assessed in terms of their cardiac intervals: 16–29, 30–35, and 36–41 weeks. The time intervals (Systolic Time Interval, STI), (Isovolumic Relaxation Time, IRT), and (Pre-ejection Period, PEP) were found to change significantly (p < 0.05) across the three age groups. The evaluation of fetal cardiac performance can be enhanced, given that these findings can be leveraged as sensitive markers throughout the process.

Alterations in Maternal-Fetal Heart Rate Coupling Strength and Directions in Abnormal Fetuses

Because fetal gas exchange takes place via the maternal placenta, there has been growing interests in investigating the patterns and directions of maternal-fetal cardiac coupling to better understand the mechanisms of placental gas transfer. We recently reported the evidence of short-term maternal–fetal cardiac couplings in normal fetuses by using Normalized Short Time Partial Directed Coherence (NSTPDC) technique. Our results have shown weakening of coupling from fetal heart rate to maternal heart rate as the fetal development progresses while the influence from maternal to fetal heart rate coupling behaves oppositely as it shows increasing coupling strength that reaches its maximum at mid gestation. The aim of this study is to test if maternal-fetal coupling patterns change in various types of abnormal cases of pregnancies. We applied NSTPDC on simultaneously recorded fetal and maternal beat-by-beat heart rates collected from fetal and maternal ECG signals of 66 normal and 19 abnormal pregnancies. NSTPDC fetal-to-maternal coupling analyses revealed significant differences between the normal and abnormal cases (normal: normalized factor (NF) = −0.21 ± 0.85, fetus-to-mother coupling area (A_fBBI→ mBBI) = 0.44 ± 0.13, mother-to-fetus coupling area (A_mBBI→ fBBI) = 0.46 ± 0.12; abnormal: NF = −1.66 ± 0.77, A_fBBI→ mBBI = 0.08 ± 0.12, A_mBBI→ fBBI = 0.66 ± 0.24; p < 0.01). In conclusion, maternal-fetal cardiac coupling strength and direction and their associations with regulatory mechanisms (patterns) of developing autonomic nervous system function could be novel clinical markers of healthy prenatal development and its deviation. However, further research is required on larger samples of abnormal cases.

Genetic Associations With Diabetic Retinopathy and Coronary Artery Disease in Emirati Patients With Type-2 Diabetes Mellitus

Aim: Type 2 Diabetes Mellitus (T2DM) is associated with both microvascular complications such as diabetic retinopathy (DR), and macrovascular complications like coronary artery disease (CAD). Genetic risk factors have a role in the development of these complications. In the present case-control study, we investigated genetic variations associated with DR and CAD in T2DM patients from the United Arab Emirates. Methods: A total of 407 Emirati patients with T2DM were recruited. Categorization of the study population was performed based on the presence or absence of DR and CAD. Seventeen Single Nucleotide Polymorphisms (SNPs), were selected for association analyses through search of publicly available databases, namely GWAS catalog, infinome genome interpretation platform and GWAS Central database. A multivariate logistic regression test was performed to evaluate the association between the 17 SNPs and DR, CAD, or both. To account for multiple testing, significance was set at p < 0.00294 using the Bonferroni correction. Results: The SNPs rs9362054 near the CEP162 gene and rs4462262 near the UBE2D1 gene were associated with DR (OR = 1.66, p = 0.001; OR = 1.37, p = 0.031; respectively), and rs12219125 near the PLXDC2 gene was associated (suggestive) with CAD (OR = 2.26, p = 0.034). Furthermore, rs9362054 near the CEP162 gene was significantly associated with both complications (OR = 2.27, p = 0.0021). The susceptibility genes for CAD (PLXDC2) and DR (UBE2D1) have a role in angiogenesis and neovascularization. Moreover, association between the ciliary gene CEP162 and DR was established in terms of retinal neural processing, confirming previous reports. Conclusions: The present study reports associations of different genetic loci with DR and CAD. We report new associations between CAD and PLXDC2, and DR with UBE2D1 using data from T2DM Emirati patients.

Clinical and genetic associations of renal function and diabetic kidney disease in the United Arab Emirates: a cross-sectional study

OBJECTIVES

Within the Emirati population, risk factors and genetic predisposition to diabetic kidney disease (DKD) have not yet been investigated. The aim of this research was to determine potential clinical, laboratory and reported genetic loci as risk factors for DKD.

RESEARCH DESIGN AND METHODS

Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD, and clinical and laboratory data were obtained. Following adjustments for possible confounders, a logistic regression model was developed to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for age and gender, were then used to study the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with vitamin D (25-OH cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine and 73 SNPs with blood urea.

RESULTS

Patients with DKD, as compared with those without the disease, were mostly men (52%vs38% for controls), older (67vs59 years) and had significant rates of hypertension and dyslipidaemia. Furthermore, patients with DKD had T2DM for a longer duration of time (16vs10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (p=0.02, OR=3.12, 95% CI 1.21 to 8.02). Among the replicated associations of the genetic loci with different renal function traits, the most notable included SHROOM3 with levels of serum creatinine, eGFR and DKD (P_adjusted=0.04, OR=1.46); CASR, GC and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.

CONCLUSIONS

Associations were found between several genetic loci and risk markers for DKD, which may influence kidney function traits and DKD in a population of Arab ancestry.

Investigating the Relationship between the Ratings of Perceived Exertion and Tone-Entropy of Heart Rate Variability during a Graded Exercise

This study explored the autonomic nervous system (ANS) adaptation in relation to exercise and how this correlates with the ratings of perceived exertion (Borg-RPE) over four ranges 6-8; 9-12; 13-16; 17-20, by using the time domain parameters and the multi-lag Tone-Entropy (T-E) of heart rate variability (HRV). ECG signals were collected from ten subjects who were recruited to participate in a graded exercise protocol on a treadmill. Results showed that SDNN and RMSSD decreased from lower to higher Borg-RPE, indicating a decrease in HRV. Entropy significantly decreased along the first 3 Borg-RPE ranges but increased in the recovery phase in which Tone values became negative (high HRV). As Borg-RPE values increased to the 17-20 range, Tone values decreased and Entropy increased compared to the 13-16 interval suggesting vagal predominance as opposed to HRV time domain results. The highest value of Tone was observed in the Borg-RPE 9-12 range indicating paramount sympathetic dominance. The use of multi-lag in T-E 2D space improved the separation of HRV with reference to the Borg-RPE intervals (p<0.05), except between the 13-16 and 17-20 ranges of the Borg-RPE. Results highlighted the analytical power of T-E in assessing both HRV changes and the sympatho-vagal balance throughout a graded exercise. Potentially, T-E analysis can be applied to assess rehabilitation settings and to get further information on ANS modulation at high exercise intensities.