One highly valuable tool for diagnosing heart diseases is the Electrocardiogram (ECG). This method involves recording the electrical signals emitted by the heart, using electrodes placed on the chest and various organs. The primary objective of this project is to employ digital signal pro-cessing of ECG signals to classify and diagnose heart diseases. The conditions that can be classi-fied through this digital processing of ECG signals encompass arrhythmia, atrioventricular block, cardiomyopathy, bundle branch block, and more. Therefore, this study primarily focuses on the classification and diagnosis of some of these heart diseases. The Pan-Tompkins algorithm is em-ployed in this study to detect the QRS complex in the ECG signals. Various classification algo-rithms, such as K-nearest neighbor, support vector machine, decision tree, and neural network, have been utilized to classify these signals. The digital processing of ECG signals is conducted using MA‌TLAB software. The ECG signals utilized in this project were sourced from the PTB Diagn‌ostic database available at physionet.org. Ultimately, the K-NN classifier with an F-criterion of 0.88 and a K-value of 20 demonstrated the most robust performance in classifying these heart diseases. پرونده مقاله

In this paper, we propose a new multi-resolution wavelet based mesh free method for numerical analysis of electromagnetic field problems. In problems with variable object geometries or mechanical movements, the mesh free methods yield more accurate simulation results compared to the finite element approach in solving the inverse problem, because they are based on a set of nodes without using the connectivity of the elements. The wavelet based mesh free method requires effectively no local integration in the vicinity of nodes in numerical implementations. Moreover, wavelets give a more efficient approximation using multi-resolution analysis. On the other hand, boundary condition constraints are difficult to be applied on the wavelet based mesh free method. In order to apply boundary and interface conditions, we utilize a new form of jump functions in the set of basic functions. The boundary and interface conditions are applied effectively using the suggested slope jump functions. The simulation results of the proposed method using two different jump functions in solving some simple boundary problems are compared. The results are validated by analytical solutions. The results of this study can be used in future for inverse problem of Magnetic resonance electrical impedance tomography (MREIT) studies as an imaging technique for reconstructing the cross-sectional conductivity distribution of a human brain or body using EIT technique integrated with the MRI. پرونده مقاله