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Segmentation of Electrocardiographic Signals Using Deep Learning Methods
Hejč, Jakub ; Černý, Martin (referee) ; Halámek, Josef (referee) ; Kolářová, Jana (advisor)
The thesis deals with deep learning methods for the segmentation of surface and intracardiac electrocardiographic recording with focus on atrial activity. The theoretical part introduces current segmentation aproaches of electrocardiographic signals. Issues related to the development of deep learning models in context of standard ECG databases were also discussed. We proposed a pipeling for processing multimodal electrophysiology data from interventional procedures in order to build reliable training datasets. A deep model for segmentation of intracardiac recordings based on a modified residual architecture was proposed. A series of experiments was conducted to evaluate the effect of both model and dataset properties on segmentation quality. The annotation methodology of recordings with atrial fibrillation proved to be a crucial factor. Properties of loss function and type of data augmentation were revealed as secondary important parameters. A novel P wave segmentation method for incomplete references was proposed in the thesis. The approach was inspired by the deep contrast learning. It was modified to distinguish local segments of signals at different levels of abstraction of the extracted feature maps. Results were analyzed using standard quality metrics and post-hoc visual analysis. In some cases, a statistical comparison of experiments for different settings was performed. The results of the work showed that it is possible to use intracardiac signals for embedding a vector representation of local atrial activation into deep models.
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Exercise-based predictors of atrial fibrillation recurrence in patients undergoing catheter ablation.
Mátych, Martin ; Pešl, Martin (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation (AF) is the most frequently treated heart arrhythmia. Radiofrequency catheter ablation is a treatment option with a success rate ranging from 60 % to 80 % for paroxysmal AF. This work aimed to determine parameters associated with AF recurrence to identify high-risk patients. Data from 98 patients who underwent pulmonary vein isolation were analyzed. Out of these patients, 19 experienced AF recurrence. Exercise and echocardiographic parameters differed significantly between the recurrence and non-recurrence groups and were used in regression analysis. Peak oxygen consumption (pVO2) was found to be a strong predictor of AF recurrence after adjusting for gender and age (hazard ratio 0.43). Four parameters were identified as the ideal combination in multivariable analysis: pVO2, septal peak late diastolic mitral annulus velocity, post-exercise systolic blood pressure, and left atrial volume index. These findings highlight the importance of stress and echocardiographic parameters in predicting the success of ablation procedures.
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Software for ECG analysis
Plch, Vít ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
This bachelor thesis deals with design and construction of tool for analysing electrocardiograms. The theoretical part deals with the origin of the action potential, propagation of the action potential through conduction system of the heart, failure of electrical impulse propagation through the con-duction system; different aspects of disorders, which can be found in experimental electrograms recorded from animal isolated hearts (database of electrograms, the Department of Biomedical En-gineering, FEEC, BUT), are also discussed. Software for electrograms draw and annotation available on the DBME, namely EG_Anotation and EG_RR_View, are described. As a result, the design and construct of ECG_ANN, the tool for the electrograms annotation, is proposed with regard to the advantages and disadvantages of mentioned software. At the end of the bachelor thesis there are the guide for ECG_ANN and discussion about problems which appears in design and construct of this tool.
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Segmentation of Hidden P Waves Using Deep Learning Methods
Boudová, Markéta ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
The aim of this thesis is segmentation of P waves in ECG signals. The theoretical part of the thesis describes the physiology of the heart and the basics of deep learning methods. Preprocessing of the signals is performed and neural network U-Net is implemented in the Python software environment in the practical part. Afterwards, optimization of network architecture is performed in order to reduce model complexity. Lastly the success rate of the model is evaluated.
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Shortwave diathermy
Faltýnková, Květoslava ; Hejč, Jakub (referee) ; Kolářová, Jana (advisor)
Bachelor thesis deals with high-frequency heating of tissues and the influence of electromagnetic fields on living tissue. Describes the capacitive and inductive method, which are compared by calculating. The goal is to create an application for simulation of vysokofrekvečního heating of tissues in MATLAB. The application includes four models. The output of the work are simulated waveforms of the intensities and heats the tissues.
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Delineation of experimental ECG data
Hejč, Jakub ; Janoušek, Oto (referee) ; Vítek, Martin (advisor)
This thesis deals with a proposition of an algorithm for QRS complex and typical ECG waves boundaries detection. It incorporates a literature research focused on heart electrophysiology and commonly used methods for ECG fiducial points detection and delineation. Out of the presented methods an algorithm based on a continuous wavelet transform is implemented. Detection and delineation algorithm is tested on CSE standard signal database towards references determined both manually and automatically. Obtained results are compared to other congenerous methods. The diploma thesis is further concerned with an algorithm modification for experimental electrocardiograms of isolated rabbit hearts. Recording specifics of these data are introduced. Additionally, based on time and frequency analysis, particular modifications of the algorithm are proposed and realized. Due to a large extent of records functionality is verified on randomly selected database samples. Efficiency of the modified algorithm is evaluated through manually annotated references.
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Deep Neural Network for Detection of Atrial Fibrillation
Budíková, Barbora ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.
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Deep learning based QRS delineator
Malina, Ondřej ; Hejč, Jakub (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Simulation of pharmacokinetic models
Hejč, Jakub ; Jiřík, Radovan (referee) ; Mézl, Martin (advisor)
The theoretical part of this project is occupied with analysis of pharmacokinetic actions and also basic attributes of mathematical models used in pharmacokinetics. This description is mainly focused on models used for perfusion imaging methods. The aim of this project is to create an algorithm that simulates chosen models based on assigned parameters and also an algorithm that serves to fit experimentally measured data with a chosen model with a calculation of basic pharmacokinetic parameters. The next step of this solution is graphic interface realization which enables a full use of created algorithm in more accessible surroundings for the user. The result of this work is a program that can be used to obtain real data parameters and as well as a visual sample of the influence of these parameters on a process in a chosen functions.
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Analysis of Atrial Fibrillation Heart Rate Dynamics
Tesařová, Tereza ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Bachalor thesis focuses on clinical tracking of heart rate abnormalities to specify the level of variability in diverse-lengthy ECG signals, with a direct way to analyze atrial fibrillation and accuracy increasing its detection of actual available methods. The principle of heart electrophysiology and also the impulse genesis is mentioned in a introductory part of work, including elementary classification of cardiac arrhytmias and their deflection from normal sinus rhythm. The physiological analysis of atrial fibrilation dynamic structure helps with the application of detection algorithms in a MATLAB program and sets an underlying basis for an appropriate discussion in a final statistical ROC analysis.
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