Load-Independent And Regional Measures Of Cardiac Function Via Real-Time Mri

LOAD-INDEPENDENT AND REGIONAL MEASURES OF CARDIAC FUNCTION VIA REAL-TIME MRI Francisco Jose Contijoch Robert C Gorman, MD Expansion of infarcted tissue during left ventricular (LV) remodeling after a myocardial infarction is associated with poor long-term prognosis. Several interventions have been developed to limit infarct expansion by modifying the material properties of the infarcted or surrounding borderzone tissue. Measures of myocardial function and material properties can be obtained non-invasively via imaging. However, these measures are sensitive to variations in loading conditions and acquisition of load-independent measures have been limited by surgically invasive procedures and limited spatial resolution. In this dissertation, a real-time magnetic resonance imaging (MRI) technique was validated in clinical patients and instrumented animals, several technical improvements in MRI acquisition and reconstruction were presented for improved imaging resolution, load-independent measures were obtained in animal studies via non-invasive imaging, and regional variations in function were measured in both na�ve and post-infarction animals. Specifically, a golden-angle radial MRI acquisition with non-Cartesian SENSE-based reconstruction with an exposure time less than 95 ms and a frame rate above 89 fps allows for accurate estimation of LV slice volume in clinical patients and instrumented animals. Two technical developments were pursued to improve image quality and spatial resolution. First, the slice volume obtained can be used as a self-navigator signal to generate retrospectively-gated, high-resolution datasets of multiple beat morphologies. Second, cross-correlation of the ECG with previously observed values resulted in accurate interpretation of cardiac phase in patients with arrhythmias and allowed for multi-shot imaging of dynamic scenarios. Synchronizing the measured LV slice volume with an LV pressure signal allowed for pressure-volume loops and corresponding load-independent measures of function to be obtained in instrumented animals. Acquiring LV slice volume at multiple slice locations revealed regional differences in contractile function. Motion-tracking of the myocardium during real-time imaging allowed for differences in contractile function between normal, borderzone, and infarcted myocardium to be measured. Lastly, application of real-time imaging to patients with arrhythmias revealed the variable impact of ectopic beats on global hemodynamic function, depending on frequency and ectopic pattern. This work established the feasibility of obtaining load-independent measures of function via real-time MRI and illustrated regional variations in cardiac function

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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

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Detection and measurement and of repolarisation features in atrial fibrillation and healthy subjects

Major cardiac organisations recommended U wave abnormalities should be reported during ECG interpretation. However, U waves cannot be measured in patients with atrial fibrillation (AF) due to the obscuring fibrillatory wave.The first aim of the research was to provide a validated algorithm to clean the ECGs of AF patients by removing the atrial fibrillatory waves so that the characteristics of ventricle repolarisation components, U and T waves, could be detected and measured accurately without fibrillatory wave contamination.Having established a validated algorithm to measure the waveform features, the second aim was to use this algorithm to investigate the effect of beat interval dependency on the repolarisation waves, especially U waves, during AF and to compare them to those in sinus rhythm (SR) of healthy subjects. The research could provide mechanistic insight into the origin of U waves since AF is unique in its rapidly changing ventricular beat intervals. The preceding beat interval has a direct impact on ventricular filling dynamics and hence also on mechano-electrical coupling, one of the leading hypotheses of U wave genesis.Algorithms were developed to remove the contaminating fibrillatory waves in AF recordings and to measure features of the ventricular repolarisation waves.The ventricular repolarisation features, U and T waves, are measurable and dependent on preceding beat interval in AF and SR. The beat interval dependency of repolarisation features, especially the U wave, supported the mechano-electrical hypothesis during AF and SR.The research provides tools to facilitate the detection and reporting of U waves and their abnormalities in AF patients and provides mechanistic insight into rate dependency of ventricular repolarisation features

Abnormal ECG search in long-term electrocardiographic recordings from an animal model of heart failure

Heart failure is one of the leading causes of death in the United States. Five million Americans suffer from heart failure. Advances in portable electrocardiogram (ECG) monitoring systems and large data storage space allow the ECG to be recorded continuously for long periods. Long-term monitoring could potentially lead to better diagnosis and treatment if the progression of heart failure could be followed. The challenge is to analyze the sheer mass of data. Manual analysis using the classical methods is impossible. In this dissertation, a framework for analysis of long-term ECG recording and methods for searching an abnormal ECG are presented.;The data used in this research were collected from an animal model of heart failure. Chronic heart failure was gradually induced in rats by aldosterone infusion and a high Na and low Mg diet. The ECG was continuously recorded during the experimental period of 11-12 weeks through radiotelemetry. The ECG leads were placed subcutaneously in lead-II configuration. In the end, there were 80 GB of data from five animals. Besides the massive amount of data, noise and artifacts also caused problems in the analysis.;The framework includes data preparation, ECG beat detection, EMG noise detection, baseline fluctuation removal, ECG template generation, feature extraction, and abnormal ECG search. The raw data was converted from its original format and stored in a database for data retrieval. The beat detection technique was improved from the original algorithm so that it was less sensitive to signal baseline jump and more sensitive to beat size variation. A method for estimating a parameter required for baseline fluctuation removal is proposed. It provides a good result on test signals. A new algorithm for EMG noise detection was developed using morphological filters and moving variance. The resulting sensitivity and specificity are 94% and 100%, respectively. A procedure for ECG template generation was proposed to capture gradual change in ECG morphology and manage the matching process if numerous ECG templates are created. RR intervals and heart rate variability parameters are extracted and plotted to display progressive changes as heart failure develops. In the abnormal ECG search, premature ventricular complexes, elevated ST segment, and split-R-wave ECG are considered. New features are extracted from ECG morphology. The Fisher linear discriminant analysis is used to classify the normal and abnormal ECG. The results provide classification rate, sensitivity, and specificity of 97.35%, 96.02%, and 98.91%, respectively

Intimations on the Pathophysiology of Human Preterm Labor: The Unique Actions of Nitric Oxide in the Myometrium and the Consequences of its Dysregulation

Approximately 12% of all infants are born prematurely in the United States, costing in excess of 26 billion dollars annually. About half of those preterm births are the result of spontaneous preterm labor (sPTL), which is idiopathic in nature. One of the reasons so many cases of sPTL result in preterm birth is because tocolytics, which are drugs that prevent or halt labor, are only effective at delaying birth by 48-hours. This failure of tocolytics is due in part to the unique nature of uterine smooth muscle. Specifically, we have found that global cGMP accumulation, or depletion, has little effect on nitric oxide-mediated myometrial relaxation. This observation has generally been overlooked during tocolytic development in favor of pursuing therapeutics that modulate canonical pathways; however, this peculiarity of the myometrium may reveal the importance of the direct action of nitric oxide to modify proteins via S-nitrosation, a labile posttranslational modification whose dysregulation is associated with many diseases. Unlike term human myometrium, nitric oxide’s effects are not only blunted in sPTL myometrium, but global protein S-nitrosations are also diminished, suggesting a dysfunctional response to nitric oxide-mediated protein S-nitrosation. Our study of S-nitrosoglutathione reductase (GSNOR), an enzyme that degrades the common endogenous form of nitric oxide, S-nitrosoglutathione (GSNO), reveals increased expression of the enzyme in sPTL myometrium, associated with decreased total protein S-nitrosation. Inhibition of GSNOR by N6022 relaxes myometrial tissue,iindicating the importance of nitric oxide donors and protein S-nitrosation in myometrial quiescence. GSNO, which can trans-S-nitrosate proteins, also alters acto-myosin ATP-ase activity, increases TREK-1 outwardly rectifying potassium currents, and increases myosin light chain kinase activity. Taken together, these findings offer novel explanations for nitric oxide-mediated relaxation in myometrium, and provide evidence for the effectiveness of a new class of tocolytics