In this article a three-dimensional mathematical model of radiofrequency ablation during open-heart surgery is presented. It was developed to study temperature field distribution into myocardial tissue. This model uses an anatomically correct 3D model for the left atrium, obtained by magnetic resonance imaging (MRI) processing of a patient; takes into account thermoelectric characteristic differences depending on the area of electric current application; considers cooling by the air flow. An ex-vivo experiment on the pig’s heart was performed where the depth of myocardium tissue damage was measured for the model validation. It was shown that the deviation of the model data from the experiment is within the limits of instrumental measurement error. The developed model is proposed to be used for heart ablation procedures planning, or new equipment development.

Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmented communication frameworks.

In the diagnosis of many disease entities directly or indirectly related to disorders of respiratory parameters and heart disease, an important support would be to estimate the temporal changes in these parameters (most often respiratory wave (RW) and respiratory rate (RR)) on the basis the results of measurements of other physiological parameters of the patient. Such a possibility exists during ECG examination. The paper presents three methods for estimating RWand RR using ECG signal processing. The three procedures developed are shown: using Savitzky–Golay filtering (S-G), the ECG-Derived Respiration method (EDR) and the Respiratory Sinus Arrhythmia Analysis method (RSA). It must be clearly stated that the proposed methods are not designed to fully diagnose the patient’s respiratory function, but they can be applied to detect some conditions that are difficult to diagnose when performing an ECG, such as sleep-disordered breathing. The obtained results of the analysis were compared with those obtained from a dedicated measurement system developed by the authors. The second part of the paper will show the results of preliminary clinical verification of the developed analysis methods, taking into account the physiological parameters of the patient.