Curvature analysis of cardiac excitation wavefronts

Comparative study of ANN and fuzzy classifier for forecasting electrical activity of heart to diagnose Covid-19

Covid-19 is a dangerous communicable virus which lets down the world economy. Severe respiratory syndrome SARS-COV-2 leads to Corona Virus Disease (COVID-19) and has the capability of transmission through human-to-human and surface-to-human transmission leads the world to catastrophic phase. Computational system based biological signal analysis helps medical officers in handling COVID-19 tasks like ECG monitoring at Intensive care, fatal ventricular fibrillation, etc., This paper is on diagnosing heart dysfunctions such as tachycardia, bradycardia, ventricular fibrillation, cardiac arrhythmia using fuzzy relations and artificial intelligence algorithm. In this study, the heart pulse base signal and features like spectral entropy, largest lyapunov exponent, Poincare plot and detrended fluctuation analysis are extracted and presented for classification purpose. The RR intervals of Poincare plot summarize RR time series obtained from an ECG in one picture, and a time interval quantities derives information duration of HRV. This analysis eases the prediction of heart rate fluctuation due to Covid or other heart disorders. The better accuracy level in diagnosing heart pulse irregularity using Artificial Neural network(ANN) is an integer value (0 to 4)but for Fuzzy Classifier, it is 0.8 to 0.9.The processing time for analyzing heart dysfunctionalties is 0.05 s using ANN which is far better than Fuzzy classifier.

Non-Invasive Assessment Method Using Thoracic-Abdominal Profile Image Acquisition and Mathematical Modeling with Bezier Curves

The study was performed at Brasov County Hospital, in the Internal Medicine, Diabetes, Gastroenterology and Cardiology Wards with the collaboration of Transylvania University of Brasov, as a study approved by the Ethical Board of the university. The study aimed at assessing the connection between the anthropometric parameters of abdominal adiposity (measured by means of an original experiment designed to determine the curvature of the thoracic-abdominal adiposity for the patients and processed by help of a mathematical model based upon Bezier curves geometry) and the fat load of the liver (assessed by ultrasound by measuring the diameters of both hepatic lobes) for the patients diagnosed with Non-Alcoholic Fatty Liver Disease (NAFLD). The existence of ten types of thoracic-abdominal curves profiles were statistically analyzed in order to evaluate in a simple manner the liver size in NAFLD. The method of diagnosis is based on an easily reproduced experiment, it is original, innovative, non-invasive, and cost-effective. Can be implemented anywhere in the world, there is no need for investment, only for determining the profile of the belly.

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization

BACKGROUND AND OBJECTIVE

Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors.

METHODS

The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter.

RESULTS

Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible.

CONCLUSIONS

This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns.

Dynamical mechanism of atrial fibrillation: A topological approach

While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets' hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead, this complexity is maintained as a dynamical balance between wave coalescence-a unique, previously unidentified, topological process that increases the number of wavelets-and wave collapse-a different topological process that decreases their number.