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Stochastic models of the slow/fast type of atrioventricular nodal reentrant tachycardia and tachycardia with conduction aberration

100%

Jackowska-Zduniak B.

2022

tom Vol. 32, no. 3

429--440

Models are proposed to describe the heart’s action potential. A system of stochastic differential equations is used to recreate pathological behaviour in the heart such as atrioventricular nodal reentrant tachycardia (AVNRT) and also AVNRT with conduction aberration. Part of the population has abnormal accessory pathways: fast and slow. An additional pathway is not always induced, since the deterministic model is not proper due to a stochasticity in this process. Introduction of a stochastic term allows modelling a pre-excitation perturbation (such as unexpected excitation by premature contractions in atrium (PAC)) which triggers the mechanism of AVNRT. Also, a system of AVNRT with additional conduction aberration, which is a rare type of arrhythmia, is considered. The aim of this work is to propose a mathematical model superior to the deterministic one that recreates this disease better and allows understanding its mechanism and physical dependencies, which may help to propose a new therapy of AVNRT. Results are illustrated with numerical solutions.

A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques

80%

Kavya L. , Karuna Y. , Saritha S. , Prakash A. J. , Patro K. K. , Sahoo S. P. , Tadeusiewicz R. , Pławiak P.

International Journal of Applied Mathematics and Computer Science

2024

tom Vol. 34, no. 3

485--511

An electrocardiogram (ECG) is an essential medical tool for analyzing the functioning of the heart. An arrhythmia is a deviation in the shape of the ECG signal from the normal sinus rhythm. Long-term arrhythmias are the primary sources of cardiac disorders. Shockable arrhythmias, a type of life-threatening arrhythmia in cardiac patients, are characterized by disorganized or chaotic electrical activity in the heart’s lower chambers (ventricles), disrupting blood flow throughout the body. This condition may lead to sudden cardiac arrest in most patients. Therefore, detecting and classifying shockable arrhythmias is crucial for prompt defibrillation. In this work, various machine and deep learning algorithms from the literature are analyzed and summarized, which is helpful in automatic classification of shockable arrhythmias. Additionally, the advantages of these methods are compared with existing traditional unsupervised methods. The importance of digital signal processing techniques based on feature extraction, feature selection, and optimization is also discussed at various stages. Finally, available databases, the performance of automated algorithms, limitations, and the scope for future research are analyzed. This review encourages researchers’ interest in this challenging topic and provides a broad overview of its latest developments.