Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary Artery Disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the Heart Rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD

Ocular and systemic markers for vascular function in those at risk of type 2 diabetes mellitus and cardiovascular disease

The devastating impact of Type 2 Diabetes Mellitus (T2DM) -related morbidity and mortality on global healthcare is escalating with higher prevalences of obesity, poor diet, and sedentary lifestyles. Therefore, the clinical need for early diagnosis and prevention in groups of high-risk individuals is necessary. The purpose of this thesis was to investigate the use of surrogate markers, namely retinal vascular function, to determine future vascular endothelial dysfunction, atherosclerosis, large vessel disease and cardiovascular risk in certain groups. This namely covered normoglycaemic and normotensive South Asians (SAs), those with Impaired-Glucose Tolerance (IGT) and individuals with a familial history (FH) of T2DM. Additionally the effect of overweight and obesity was studied. The techniques and modified protocols adopted for this thesis involved the investigation of endothelial function by means of vascular reactivity at the ocular and systemic level. Furthermore, the relationships between retinal and systemic function with circulating markers for endothelial cell function and cardiovascular risk markers were explored. The principal studies and findings of the research were: Vascular Function in Normoglycaemic Individuals with and without a FH of T2DM WE FH individuals exhibited higher levels of total cholesterol levels that correlated well with the retinal arterial dilation amplitude to flicker light stimulus. However this did not extend to noticeable differences in markers for endothelial cell damage and impaired retinal and systemic function. Vascular Function in Normoglycaemic South-Asians vs. White-Europeans without a FH and Vascular Disturbances Compared to healthy WEs (normo -glycaemic and -tensive), SA participants exhibited levels of dyslipidaemia and a state of oxidative stress that extended to impaired vascular function as detected by reduced brachial artery flow-mediated dilation, slower retinal arterial vessel dilation reaction times (Appendix 3) and steeper constriction profiles. Furthermore, gender sub-group analysis presented in a sub-chapter shows that SA males demonstrated 24-hour systemic blood pressure (BP) and heart rate variability (HRV) abnormalities and heightened cardiovascular disease (CVD) risk. Vascular Function in Individuals Newly Diagnosed with IGT as compared to Normoglycaemic Healthy Controls Newly-diagnosed WE and SA IGT patients showed a greater risk for CVD and T2DM progression by means of 24-hour BP abnormalities, dyslipidaemia, increased carotid artery intimal-media thickness (c-IMT), Framingham scores and cholesterol ratios. Additionally, pre-clinical markers for oxidative stress and endothelial dysfunction, as evident by significantly lower levels of plasma glutathione and increased levels of von-Willebrand factor in IGT individuals, extended to impaired vascular systemic and retinal function compared to normal controls. This originally shows retinal, systemic and biochemical disturbances in newly-diagnosed IGT not previously reported before. Vascular Function in Normal, Overweight and Obese Individuals of SA and WE Ethnicity In addition to the intended study chapters, the thesis also investigated the influence of obesity and overweight on vascular function. Most importantly, it was found for the first time that compared to lean individuals it was overweight and not obese individuals that exhibited signs of vascular systemic and ocular dysfunction that was evident alongside markers of atherosclerosis, CVD risk and endothelial damage

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

Comparative analysis of classification algorithms for chronic kidney disease diagnosis

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure

Ocular and systemic markers for vascular function in those at risk of type 2 diabetes mellitus and cardiovascular disease

The devastating impact of Type 2 Diabetes Mellitus (T2DM) -related morbidity and mortality on global healthcare is escalating with higher prevalences of obesity, poor diet, and sedentary lifestyles. Therefore, the clinical need for early diagnosis and prevention in groups of high-risk individuals is necessary. The purpose of this thesis was to investigate the use of surrogate markers, namely retinal vascular function, to determine future vascular endothelial dysfunction, atherosclerosis, large vessel disease and cardiovascular risk in certain groups. This namely covered normoglycaemic and normotensive South Asians (SAs), those with Impaired-Glucose Tolerance (IGT) and individuals with a familial history (FH) of T2DM. Additionally the effect of overweight and obesity was studied. The techniques and modified protocols adopted for this thesis involved the investigation of endothelial function by means of vascular reactivity at the ocular and systemic level. Furthermore, the relationships between retinal and systemic function with circulating markers for endothelial cell function and cardiovascular risk markers were explored. The principal studies and findings of the research were: Vascular Function in Normoglycaemic Individuals with and without a FH of T2DM WE FH individuals exhibited higher levels of total cholesterol levels that correlated well with the retinal arterial dilation amplitude to flicker light stimulus. However this did not extend to noticeable differences in markers for endothelial cell damage and impaired retinal and systemic function. Vascular Function in Normoglycaemic South-Asians vs. White-Europeans without a FH and Vascular Disturbances Compared to healthy WEs (normo -glycaemic and -tensive), SA participants exhibited levels of dyslipidaemia and a state of oxidative stress that extended to impaired vascular function as detected by reduced brachial artery flow-mediated dilation, slower retinal arterial vessel dilation reaction times (Appendix 3) and steeper constriction profiles. Furthermore, gender sub-group analysis presented in a sub-chapter shows that SA males demonstrated 24-hour systemic blood pressure (BP) and heart rate variability (HRV) abnormalities and heightened cardiovascular disease (CVD) risk. Vascular Function in Individuals Newly Diagnosed with IGT as compared to Normoglycaemic Healthy Controls Newly-diagnosed WE and SA IGT patients showed a greater risk for CVD and T2DM progression by means of 24-hour BP abnormalities, dyslipidaemia, increased carotid artery intimal-media thickness (c-IMT), Framingham scores and cholesterol ratios. Additionally, pre-clinical markers for oxidative stress and endothelial dysfunction, as evident by significantly lower levels of plasma glutathione and increased levels of von-Willebrand factor in IGT individuals, extended to impaired vascular systemic and retinal function compared to normal controls. This originally shows retinal, systemic and biochemical disturbances in newly-diagnosed IGT not previously reported before. Vascular Function in Normal, Overweight and Obese Individuals of SA and WE Ethnicity In addition to the intended study chapters, the thesis also investigated the influence of obesity and overweight on vascular function. Most importantly, it was found for the first time that compared to lean individuals it was overweight and not obese individuals that exhibited signs of vascular systemic and ocular dysfunction that was evident alongside markers of atherosclerosis, CVD risk and endothelial damage.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Dissecting the etiology of atrial fibrillation:A population perspective on risk factors and sex differences

In this thesis we examined various risk factors and sex differences and their influence on the atrial fibrillation development in the general population. We found that vascular-, cardiac autonomic-, inflammatory-, traditional-, novel-, and sex-specific risk factors are implicated in the etiology of atrial fibrillation