The contact electrogram and its architectural determinants in atrial fibrillation

The electrogram is the sine qua non of excitable tissues, yet classification in atrial fibrillation (AF) remains poorly related to substrate factors. The objective of this thesis was to establish the relationship between electrograms and two commonly implicated substrate factors, connexin 43 and fibrosis in AF. The substrates and methods chosen to achieve this ranged from human acutely induced AF using open chest surgical mapping (Chapter 6), ex vivo whole heart Langendorff (Chapter 7) with in vivo telemetry confirming spontaneous AF in a new species of rat, the Brown Norway and finally isolated atrial preparations from an older cohort of rats using orthogonal pacing and novel co-localisation methods at sub-millimetre resolution and in some atria, optical mapping (Chapter 8). In rodents, electrode size and spacing was varied (Chapters 5, 10) to study its effects on structure function correlations (Chapter 9). Novel indices of AF organisation and automated electrogram morphology were used to quantify function (Chapter 4). Key results include the discoveries that humans without any history of prior AF have sinus rhythm electrograms with high spectral frequency content, that wavefront propagation velocities correlated with fibrosis and connexin phosphorylation ratios, that AF heterogeneity of conduction correlates to fibrosis and that orthogonal pacing in heavily fibrosed atria causes anisotropy in electrogram-fibrosis correlations. Furthermore, fibrosis and connexin 43 have differing and distinct spatial resolutions in their relationship with AF organisational indices. In conclusion a new model of AF has been found, and structure function correlations shown on an unprecedented scale, but with caveats of electrode size and direction dependence. These findings impact structure function methods and prove the effect of substrate on AF organisation.Open Acces

Cardiac Arrhythmias

The most intimate mechanisms of cardiac arrhythmias are still quite unknown to scientists. Genetic studies on ionic alterations, the electrocardiographic features of cardiac rhythm and an arsenal of diagnostic tests have done more in the last five years than in all the history of cardiology. Similarly, therapy to prevent or cure such diseases is growing rapidly day by day. In this book the reader will be able to see with brighter light some of these intimate mechanisms of production, as well as cutting-edge therapies to date. Genetic studies, electrophysiological and electrocardiographyc features, ion channel alterations, heart diseases still unknown , and even the relationship between the psychic sphere and the heart have been exposed in this book. It deserves to be read

HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved.By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60\%, F1 score of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.Comment: 23 page

Cardiac rehabilitation activities during the COVID-19 pandemic in Italy. Position Paper of the AICPR (Italian Association of Clinical Cardiology, Prevention and Rehabilitation)

The COVID-19 outbreak is having a significant impact on both cardiac rehabilitation (CR) inpatient and outpatient healthcare organization. The variety of clinical and care scenarios we are observing in Italy depends on the region, the organization of local services and the hospital involved. Some hospital wards have been closed to make room to dedicated beds or to quarantine the exposed health personnel. In other cases, CR units have been converted or transformed into COVID-19 units. The present document aims at defining the state of the art of CR during COVID-19 pandemic, through the description of the clinical and management scenarios frequently observed during this period and the exploration of the future frontiers in the management of cardiac rehabilitation programs after the COVID-19 outbreak

Non-linear dynamical analysis of biosignals

Biosignals are physiological signals that are recorded from various parts of the body. Some of the major biosignals are electromyograms (EMG), electroencephalograms (EEG) and electrocardiograms (ECG). These signals are of great clinical and diagnostic importance, and are analysed to understand their behaviour and to extract maximum information from them. However, they tend to be random and unpredictable in nature (non-linear). Conventional linear methods of analysis are insufficient. Hence, analysis using non-linear and dynamical system theory, chaos theory and fractal dimensions, is proving to be very beneficial. In this project, ECG signals are of interest. Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be fatal or cause irreparable damage to the heart when sustained over long periods of time. Hence the ability to identify arrhythmias from ECG recordings is of importance for clinical diagnosis and treatment and also for understanding the electrophysiological mechanism of arrhythmias. To achieve this aim, algorithms were developed with the help of MATLAB® software. The classical logic of correlation was used in the development of algorithms to place signals into the various categories of cardiac arrhythmias. A sample set of 35 known ECG signals were obtained from the Physionet website for testing purposes. Later, 5 unknown ECG signals were used to determine the efficiency of the algorithms. A peak detection algorithm was written to detect the QRS complex. This complex is the most prominent waveform within an ECG signal and its shape, duration and time of occurrence provides valuable information about the current state of the heart. The peak detection algorithm gave excellent results with very good accuracy for all the downloaded ECG signals, and was developed using classical linear techniques. Later, a peak detection algorithm using the discrete wavelet transform (DWT) was implemented. This code was developed using nonlinear techniques and was amenable for implementation. Also, the time required for execution was reduced, making this code ideal for real-time processing. Finally, algorithms were developed to calculate the Kolmogorov complexity and Lyapunov exponent, which are nonlinear descriptors and enable the randomness and chaotic nature of ECG signals to be estimated. These measures of randomness and chaotic nature enable us to apply correct interrogative methods to the signal to extract maximum information. The codes developed gave fair results. It was possible to differentiate between normal ECGs and ECGs with ventricular fibrillation. The results show that the Kolmogorov complexity measure increases with an increase in pathology, approximately 12.90 for normal ECGs and increasing to 13.87 to 14.39 for ECGs with ventricular fibrillation and ventricular tachycardia. Similar results were obtained for Lyapunov exponent measures with a notable difference between normal ECG (0 – 0.0095) and ECG with ventricular fibrillation (0.1114 – 0.1799). However, it was difficult to differentiate between different types of arrhythmias.Biosignals are physiological signals that are recorded from various parts of the body. Some of the major biosignals are electromyograms (EMG), electroencephalograms (EEG) and electrocardiograms (ECG). These signals are of great clinical and diagnostic importance, and are analysed to understand their behaviour and to extract maximum information from them. However, they tend to be random and unpredictable in nature (non-linear). Conventional linear methods of analysis are insufficient. Hence, analysis using non-linear and dynamical system theory, chaos theory and fractal dimensions, is proving to be very beneficial. In this project, ECG signals are of interest. Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be fatal or cause irreparable damage to the heart when sustained over long periods of time. Hence the ability to identify arrhythmias from ECG recordings is of importance for clinical diagnosis and treatment and also for understanding the electrophysiological mechanism of arrhythmias. To achieve this aim, algorithms were developed with the help of MATLAB® software. The classical logic of correlation was used in the development of algorithms to place signals into the various categories of cardiac arrhythmias. A sample set of 35 known ECG signals were obtained from the Physionet website for testing purposes. Later, 5 unknown ECG signals were used to determine the efficiency of the algorithms. A peak detection algorithm was written to detect the QRS complex. This complex is the most prominent waveform within an ECG signal and its shape, duration and time of occurrence provides valuable information about the current state of the heart. The peak detection algorithm gave excellent results with very good accuracy for all the downloaded ECG signals, and was developed using classical linear techniques. Later, a peak detection algorithm using the discrete wavelet transform (DWT) was implemented. This code was developed using nonlinear techniques and was amenable for implementation. Also, the time required for execution was reduced, making this code ideal for real-time processing. Finally, algorithms were developed to calculate the Kolmogorov complexity and Lyapunov exponent, which are nonlinear descriptors and enable the randomness and chaotic nature of ECG signals to be estimated. These measures of randomness and chaotic nature enable us to apply correct interrogative methods to the signal to extract maximum information. The codes developed gave fair results. It was possible to differentiate between normal ECGs and ECGs with ventricular fibrillation. The results show that the Kolmogorov complexity measure increases with an increase in pathology, approximately 12.90 for normal ECGs and increasing to 13.87 to 14.39 for ECGs with ventricular fibrillation and ventricular tachycardia. Similar results were obtained for Lyapunov exponent measures with a notable difference between normal ECG (0 – 0.0095) and ECG with ventricular fibrillation (0.1114 – 0.1799). However, it was difficult to differentiate between different types of arrhythmias

Handling Pain in the Coronary Care Unit

Pain is not only a symptom for warning a person for a health problem. The mechanism of pain increases the cardiac load, increases the needs of myocardium and other tissues in oxygen, thus increasing myocardial ischemia. This results in instability of patient’s hemodynamics andprobable failure of treatment’s implementation. Therefore, the relationship between pain and cardiac disease is interactive: cardiac patient feels pain because of the underlying heart disease, and the disease is worsening because of pain.   As health professionals, we must concentrate on patient’s comfort and relief. Even within the difficult working conditions of the cardiac care unit (CCU), the patient in pain should be approached with compassion and care. We should not forget that pain is the usual symptom which leads the patient in search for health services and then to the CCU. The development of a trust relationship between the health professional and the patient is the basis for the expression of pain and fears by the patient. Physical examination helps refine the differential diagnosis. Delay in treatment can lead to a substantial recidivism of the disease. The patient should be constantly encouraged to alert the personnel of any complaints of pain. The evaluation of pain through the dialogue and the clinical evaluation individualizes treatment. Administration of analgesics, tranquillizers and sedative medicines contribute to the reduction of patient’s agony and stress and help him or her to control factors causing pain. Within a multidisciplinary group, nursing personnel have a unique, important and independent role in handling pain in the CCU

MS

thesisAccurate QRS detection is essential in online computerized rhythm monitoring systems. A major cause of error in QRS detection schemes arises from artifacts superimposed on the input signal. To a lesser extent identification of P or T waves as QRS complexes can represent another source of error. In an effort to reduce the incidence of false and missed alarms generated by the rhythm monitoring system currently used in the LDS Hospital Coronary Care Unit, a project was undertaken to improve the accuracy and reliability of the QRS detection algorithm, specifically in contaminated single lead electrocardiographic data. The algorithm uses a dual scan of the sample data combined with a peak detection scheme to locate a reference point on a QRS candidate. The candidate is then checked for evidence of baseline shift or an excessively low signal-to-noise ratio. If neither of these criteria is met, the candidate is assumed to be QRS and a fiducial point is located on the complex. To assess the sensitivity and specificity of the QRS detection algorithm, an off-line evaluation was performed on forty-one patient records collected in the Coronary Care Unit. Arrhythmias included in the evaluation were fast ventricular and atrial rhythms and heart block. Over 90 percent of the data base was contaminated with excessive muscle artifacts. Of a total of 7,205 beats used in the evaluation, and positive predictive accuracy were .9641 and .9573, respectively. Of the error, 92.16 percent of the false positives and 84.17 percent of the false negatives were due to excessive noise spike superimposition on the data. None of the false positive error (.0071) was due to P or T wave misidentification of a QRS complex. These results indicate that a signal-in-noise approach to automated QRS detection is effective in identifying QRS complexes in the contaminated single lead electrocardiogram with minimal error

Computer modeling and signal analysis of cardiovascular physiology

This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches. A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker may significantly change the critical pacing period corresponding to the onset of alternans as well as the alternans’ amplitude. The influence of electro-mechanical coupling on cardiac alternans was also investigated. The study supported the earlier assumptions that discordant alternans is induced by the interaction of conduction velocity and action potential duration restitution at high pacing rates. However, mechanical contraction may influence the spatial pattern and onset of discordant alternans. Computer algorithms were developed for analysis of human physiology. The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of various cardiac abnormalities. However, disturbances and mistakes may modify physiological waves in ECG and lead to wrong diagnoses. This dissertation developed advanced signal analysis techniques and computer software to detect and suppress artifacts and errors in ECG. These algorithms can help to improve the quality of health care when integrated into medical devices or services. Moreover, computer algorithms were developed to predict patient mortality in intensive care units using various physiological measures. Models and analysis techniques developed here may help to improve the quality of health care

The utility of handheld and wearable devices in the diagnosis of cardiac arrhythmias

The aim of this thesis is to highlight the existing body of literature on the utility of wearable and handheld devices in the diagnosis and management of cardiac arrhythmias. Furthermore, the thesis investigates the accuracy and utility of the AliveCor Kardia for the detection of cardiac arrhythmias in a systematic fashion