Automated Method for the Volumetric Evaluation of Myocardial Scar from Cardiac Magnetic Resonance Images

In most western countries cardiovascular diseases are the leading cause of death, and for the survivors of ischemic attack an accurate quantification of the extent of the damage is required to correctly assess its impact and for risk stratification, and to select the best treatment for the patient. Moreover, a fast and reliable tool for the assessment of the cardiac function and the measurement of clinical indexes is highly desirable. The aim of this thesis is to provide computational approaches to better detect and assess the presence of myocardial fibrosis in the heart, particularly but not only in the left ventricle, by performing a fusion of the information from different magnetic resonance imaging sequences. We also developed and provided a semiautomatic tool useful for the fast evaluation and quantification of clinical indexes derived from heart chambers volumes. The thesis is composed by five chapters. The first chapter introduces the most common cardiac diseases such as ischemic cardiomyopathy and describes in detail the cellular and structural remodelling phenomena stemming from heart failure. The second chapter regards the detection of the left ventricle through the development of a semi-automated approach for both endocardial and epicardial surfaces, and myocardial mask extraction. In the third chapter the workflow for scar assessment is presented, in which the previously described approach is used to obtain the 3D left ventricle patient-specific geometry; a registration algorithm is then used to superimpose the fibrosis information derived from the late gadolinium enhancement magnetic resonance imaging to obtain a patientspecific 3D map of fibrosis extension and location on the left ventricle myocardium. Focus of the fourth chapter is on the left atrium, and fibrotic tissue detection for gaining insight on atrial fibrillation. In the fifth chapter some conclusive remarks are presented with possible future developments of the presented work

Causality analysis of atrial fibrillation electrograms

Proceeding of 2015 Computing in Cardiology Conference (CinC 2015), September 6-9, 2015, Nice, FranceMulti-channel intracardiac electrocardiograms (electrograms) are sequentially acquired during heart surgery performed on patients with sustained atrial fibrillation (AF) to guide radio frequency catheter ablation. These electrograms are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex electrograms). In this paper, we introduce a novel hierarchical causality analysis method for the multi-output sequentially acquired electrograms. The causal model obtained provides important information regarding delays among signals as well as the direction and strength of their causal connections. The tool developed may ultimately serve to guide cardiologists towards candidate areas for catheter ablation. Preliminary results on synthetic signals are used to validate the proposed approach.This work has been supported by the Spanish government’s projects ALCIT (TEC2012-38800-C03-01), AGES (S2010/BMD-2422), and OTOSiS (TEC2013-41718-R), and COMPREHENSION (TEC2012-38883-C02-01). D. Luengo has also been funded by the BBVA Foundation’s “I Convocatoria de Ayudas Fundación BBVA a Investigadores, Innovadores y Creadores Culturales”.Publicad

Extended segmented beat modulation method for cardiac beat classification and electrocardiogram denoising

none4noBeat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.openNasim A.; Sbrollini A.; Morettini M.; Burattini L.Nasim, A.; Sbrollini, A.; Morettini, M.; Burattini, L

Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods

Estimation of Serum Potassium and Calcium Concentrations from Electrocardiographic Depolarization and Repolarization Waveforms

Chronic kidney disease (CKD), a condition defined by a gradual decline in kidney function over time, has become a global health concern affecting between 11 and 13% of the world population [1]. As renal function declines, CKD patients gradually lose their ability to maintain normal values of potassium concentration ([K+]) in their blood. Elevated serum [K+], known as hyperkalemia, increases the risk for life-threatening arrhythmias and sudden cardiac death [2].An increase in serum [K+] outside the physiological range is commonly silent and is only detected when hyperkalemia is already very severe or when a blood test is performed. Maintenance and monitoring of [K+] in the blood is an important component in the treatment of CKD patients because therapies for hyperkalemia management in CKD patients are designed to prevent arrhythmias and to immediately lower serum [K+] to safe ranges. However, this is currently only possible by taking a blood sample and is associated with a long analysis time. Therefore it is useful to have a simple, noninvasive method to estimate serum [K+], particularly using the electrocardiogram (ECG). Indeed, variations in serum electrolyte levels have been shown to alter the electrical behavior of the heart and to induce changes in the ECG [3¿6]. However, large inter-individual variability existsin the relationship between ion concentrations and ECG features. Previous attempts to estimate serum [K+] from the ECG have therefore shown limitations [7¿9], such as not being applicable to some common types of ECG waveforms or relying on specific ECG characteristics that may present large variations not necessarily associated with hyperkalemia.The aim of this thesis is to develop novel estimates of serum [K+] that are robust enough to detect hypokalemia (reduced [K+]) or hyperkalemia in a timely manner to provide life-saving treatment. Additionally, the effect of changes in other electrolyte levels, like calcium concentration ([Ca2+]), and in heart rate are investigated. These aims are achieved by combining novel ECG signal processing techniques with in silico modeling and simulation of cardiac electrophysiology.The specific objectives are:1. Characterization of hypokalemia or hyperkalemia and hypocalcemia (reduced [Ca2+]) or hypercalcemia (elevated [Ca2+])-induced changes in ventricular repolarization from ECGs (T wave) of CKD patients. This is addressed in chapter 3 and chapter 4. In these chapters, we describe how T waves are extracted from ECGs and how we characterize changes in T waves at varying potassium, calcium and heart rate using analyses based on time warping and Lyapunov exponents. Next, univariable and multivariable regression models including markers of T wave nonlinear dynamics in combination with warping-based markers of T wave morphology are built and their performance for [K+] estimation is assessed.2. Characterization of hypo- or hyperkalemia and hypo- or hypercalcemia-induced changes in ventricular depolarization from the QRS complex of CKD patients. This is reported in chapter 5. In this chapter, we present how QRS complexes from ECGs of CKD patients are processed and how we measure changes at varying [K+], [Ca2+] and heart rate. Univariate and multivariate regression analyses including novel QRS morphological markers in combination with T wave morphological markers are performed to assess the contribution of depolarization and repolarization features for electrolyte monitoring in CKD patients.3. Identification of potential sources underlying inter-individual variability in ECG markers in response to changes in [K+] and [Ca2+]. In silico investigations of cardiac electrophysiology are conducted and ECG features are computed. Simulation results are compared with patient data. This is explained in chapter 3 using one-dimensional (1D) fibers and in chapter 6 using three-dimensional (3D) human heart-torso models. Chapter 6 includes the development of a population of realistic computational models of human ventricular electrophysiology, based on human anatomy and electrophysiology, to better understand how changes in individual characteristics influence the ECG (QRS and T wave) markers that we introduced in previous chapters. ECG waveforms are characterized by their amplitude, duration and morphology. Simulations are performed with the most realistic available techniques to model the electrophysiology of the heart and the resulting ECG. We establish mechanisms that contribute to inter-individual differences in the characterized ECG features.In conclusion, we identify several markers of ECG morphology, including depolarization and repolarization features, that are highly correlated with serum electrolyte (potassium and calcium) concentrations. ECG morphological variability markers vary significantly with [K+] and [Ca2+] in both simulated and measured ECGs, with a wide range of patterns observed for such relationships. The proportions of endocardial, midmyocardial and epicardial cells have a large impact on ECG markers, particularly for serum electrolyte concentrations out of their physiological levels. This suggests that transmural heterogeneities can modulate ECG responses to changes in electrolyte concentrations in CKD patients. Agreement between actual potassium and calcium levels and their estimates derived from the ECG is promising, with lower average errors than previously proposed markers in the literature. These findings can have major relevance for noninvasive monitoring of serum electrolyte levels and prediction of arrhythmic events in these patients.<br /