Resting Heart Rate and its Effect on Cardiovascular Disease

The purpose of the presented research and systematic review is to determine the associated impact resting heart rate has on development of cardiovascular disease. A literature review was conducted through electronic databases: PubMed, Clinical Key and Dynamed Plus. No limitation of timeframe was used when researching as few studies are available correlating resting heart rate and its associated development of cardiovascular disease. Research points to limitations of lowering resting heart rate due to adverse effects on blood pressure. Additionally, tachycardia is addressed through several methods including lifestyle modification and pharmacologic therapy. Comorbidities such as obesity, low body mass index (BMI), diabetes, hypertension and preexisting heart disease are influential factors in target heart rate therapy. Research concludes individuals benefit from a reduction in resting heart rate to less than 80 beats per minute, regardless of gender or ethnicity. Reduction of heart rate below 60 beats per minute shows no added benefit and can cause a paradoxical rise in blood pressure as a compensatory mechanism by the cardiovascular system, ultimately negating the benefits of lowered resting heart rate

Squatting, a posture test for studying cardiovascular autonomic neuropathy in diabetes.

Cardiovascular autonomic neuropathy (CAN) is a frequent complication of diabetes mellitus, which is associated with increased morbidity and mortality. It involves both the parasympathetic and sympathetic nervous systems, and may be diagnosed by classical dynamic tests with measurements of heart rate (HR) and/or arterial blood pressure (BP). An original squat test (1-min standing, 1-min squatting, 1-min standing) was used with continuous monitoring of HR and BP, using a Finapres((R)) device. This active test imposes greater postural stress than the passive head-up tilt test, and provokes large changes in BP and HR that can be analyzed to derive indices of CAN. In healthy subjects, squatting is associated with BP increases and HR decreases (abolished by atropine: SqTv index), whereas the squat-stand transition is accompanied by a deep but transient drop in BP associated with sympathetic-driven tachycardia (abolished by propranolol: SqTs index). In diabetic patients with CAN, BP increases are accentuated during squatting whereas reflex bradycardia is reduced. When standing from squatting position, the fall in BP tends to be more pronounced and orthostatic hypotension more prolonged, while reflex tachycardia is markedly dampened. The baroreflex gain, similar to that calculated during pharmacological testing with vasodilator/vasopressor agents, can be derived by plotting pulse intervals (R-R) against systolic BP levels during the biphasic response following the squat-stand transition. The slope, which represents baroreflex sensitivity, is significantly reduced in patients with CAN. This discriminatory index allows study of the natural history of CAN in a large cohort of diabetic patients.Peer reviewe

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Advances in Electrocardiograms

Electrocardiograms have become one of the most important, and widely used medical tools for diagnosing diseases such as cardiac arrhythmias, conduction disorders, electrolyte imbalances, hypertension, coronary artery disease and myocardial infarction. This book reviews recent advancements in electrocardiography. The four sections of this volume, Cardiac Arrhythmias, Myocardial Infarction, Autonomic Dysregulation and Cardiotoxicology, provide comprehensive reviews of advancements in the clinical applications of electrocardiograms. This book is replete with diagrams, recordings, flow diagrams and algorithms which demonstrate the possible future direction for applying electrocardiography to evaluating the development and progression of cardiac diseases. The chapters in this book describe a number of unique features of electrocardiograms in adult and pediatric patient populations with predilections for cardiac arrhythmias and other electrical abnormalities associated with hypertension, coronary artery disease, myocardial infarction, sleep apnea syndromes, pericarditides, cardiomyopathies and cardiotoxicities, as well as innovative interpretations of electrocardiograms during exercise testing and electrical pacing

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Producción CientíficaHeart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.Ministerio de Economía, Industria y Competitividad (TEC2011-22987)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13

Cardiac reserve during weightlessness simulation and shuttle flight

Bedrest deconditioning is suspected to reduce cardiac function. However, quantitation of subtle decreases in cardiac reserve may be difficult. Normal subjects show considerable variability in heart rate response, reflected by a relatively broadband interbeat interval power spectrum. We hypothesized that the deconditioning effects of bedrest would induce narrowing of this spectrum, reflecting a reduction in the autonomically-modulated variability in heart rate. Ten aerobically conditioned men (average 35-50 years) underwent orthostatic tolerance testing with lower body negative pressure pre-bedrest and after 10 days of bedrest, while on placebo and after intravenous atropine. Spectra were derived by Fourier analysis of 128 interbeat interval data sets from subjects with sufficient numbers of beats during matched periods of the protocol. Data suggest that atropine unmasks the deconditioning effect of bedrest in athletic men, evidenced by a reduction in interbeat interval spectral power compared with placebo. Spectral analysis offers a new means of quantitating the effects of bedrest deconditioning and autonomic perturbations on cardiac dynamics

Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p < 0.001) and multiclass (r=0.91, p < 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home