This master´s thesis deals with the analysis of experimental telemetrical ECG records with intention to determine the long-term influence of anticancer drug sunitinib on the electrical activity of heart. A laboratory rat was chosen as a model organism for the experimental study carried out at the Department of Physiology at Faculty of Medicine, Masaryk university. The sunitinib was applied to the rats at an early age and the ECG was measured with a 20-week delay using the Stellar telemetry system. To measure the effect of sunitinib on the electrical activity of the heart chambers, an analysis of the duration of the RR and QT interval and the width of the QRS complex was chosen. These parameters were detected by the wavelet transform method. Statistical analysis was performed using nonparametric tests - the Wilcoxon signed rank test, the MannWhitney test and the Friedman Test. The obtained results suggest that the use of sunitinib has no long-term effect on the observed parameters for the chosen animal model. After extension of the study, the results obtained could contribute to assess the effect of drugs on electrical activity of the human heart several decades after sunitinib treatment termination.

This diploma thesis is devoted to segmentation of ECG signal based on its quality and compression of quality segments suitable for diagnostics (in telemedicine). A completely new approach is to use accelerometer data to estimate ECG signal quality. This is possible thanks to the Bittium Faros mobile recorder. It records both the ECG signal motion – accelerometric data. A total of 34 features were extracted from accelerometric data. Using these features the predictive model was taught to classify the ECG signal into 3 quality groups according to the level of noise. Quality segments were compressed. The wavelet transform in combination with high-frequency bands zeroing and length encoding was used as a compression method.

In the past years, utilization of ECG for verification and identification in biometry is investigated. The topic is investigated in this thesis. Recordings from ECG ID database from PhysioNet and our own ECG recordings recorded using Apple Watch 4 are used for training and testing this method. Many of the existing methods have proven the possibility of using ECG for biometry, however they were using clinical ECG devices. This thesis investigates using recordings from wearable devices, specifically smart watch. 16 features are extracted from ECG recordings and a random forest classifier is used for verification and identification. The features include time intervals between fiducial points, voltage difference between fiducial points and PR intervals variability in a recording. The average performance of verification model of 14 people is TRR 96,19 %, TAR 84,25 %.

This project considers methods of construction QRS detectors. It focus in detection complexes of QRS single leads and space speed, which are calculated from three orthogonal leads. In theory was refer to various methods, which lead to design detector. It were designed two algoritms (constant and adaptive detecting threshold), which were implemented into detector and the signal was preprocessed by Hilbert transformation. Toward algoritms were completed by modification, which improved detection effectivity. Function of algoritms were tested in all signals of CSE (V2,V5,aVF).

This semestral thesis describes signal EKG, it’s origin, evaluation and device for his generation. After identification with standard course of EKG signal, was such as signal created in program language Matlab. It’s consisting of 10 sinus courses, where each of them has it’s own frequence, start phase and amplitude. Each of this part of EKG signal is beat frequence variant. Rates of it’s durations and sizes were fixed from medicin literature. Such as created program were rewritten to program language C and is basic of main program, which control signal processor. Finally was propoused preparation, which will be generated this signal. Outcome of this thesis is teoretic treatise about signal EKG, created program for generation of this course include part control and machine doing realization of biological courses.

The diploma thesis deals the cretion of the ECG signal and its potential morphology. A signal is generated using a program that is created in MATLAB. On the basis of these methods of describing the signal, was chosen method, based on the similarity of ECG with sinus respectively. triangular pattern. Generated by the program can draw the ECG signal by assignment of pulse rate, lenght of the signal, sampling rate and modifications of the waves and oscillations. One or more predefined noise can be added to the signal. Generated signal is possible to save to the format that supports program MATLAB.

The thesis deals with possibilities of using wavelet transform in applications dealing with noise reduction, primarily in the field of ECG signals denoising. We assess the impact of the various filtration parameters setting as the thresholding wavelet coefficients method, thresholds level setting and the selection of decomposition and reconstruction filter banks.. Our results are compared with the results of linear filtering. The results of wavelet Wieners filtration with pilot estimation are described below. Mainly, we tested a combination of decomposition and reconstruction filter banks. All the filtration methods described here are tested on real ECG records with additive myopotential noise character and are implemented in the Matlab environment.

This master’s thesis deals with the detection of QRS complex detection using zero crossing counts. QRS detection is an important part of the analysis of ECG signal. From the point of determining the R wave detection is based on the other waves and intervals necessary for the diagnosis of heart. This method is very effective even for very noisy signals. Implementation of the method in Matlab, and the success of detection is tested on the CSE and MIT-BIH database. The optimization algorithm is an optional value for the detector.

The goal of this work is to study the principles of delineation of ECG signals, wavelet transformation and transformation approaches to increase the number of available leads. Consequently, the knowledge was used to create delineation algorithm in Matlab. The algorithm was tested on complete CSE database. The obtained results were compared with the criteria which are set for the CSE database. In this work were realized six transformation approaches to increase the number of available leads. All of them were analyzed by delineation algorithm. In the work was examined, whether the transformation increase the efficiency of detection.

This master‘s thesis deals with the use of higher order cumulants for classification of cardiac cycles. Second-, third-, and fourth-order cumulants were calculated from ECG recorded in isolated rabbit hearts during experiments with repeated ischemia. Cumulants properties useful for the subsequent classification were verified on ECG segments from control and ischemic group. The results were statistically analyzed. Cumulants are then used as feature vectors for classification of ECG segments by means of artificial neural network.