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ECG signal classification
Smělý, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.
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Analysis of AVG signals
Janeček, David ; Balogh, Jaroslav (referee) ; Rozman, Jiří (advisor)
This semestral project deals with analytical arteriovelocitogram (AVG). In the first part I a question of collection data AVG curve. Doppler effect and principal of Doppler‘s systems whose assistance data gain is analyzed in this semestral project. In the next part I focus on methods of analytic signals AVG. This means discribing the curve, calculating coefficients or methods for correct evaluation of the signal. These analyzes should categorize the measured data into groups according to pathological changes, of which doctors will determine the next course of treatment. The final chapter deals with the method of cluster analysis to classify data measured by Doppler systems. I deal with the algorithm method and choose the best clustering procedure. In the following section I concern with the practical part. I describe the data which were provided, the program selected for the analysis and the description of the alghoritm. The last chapter describes the results I’ve obtained.
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Analysis of AVG signals
Janeček, David ; Balogh, Jaroslav (referee) ; Rozman, Jiří (advisor)
This semestral project deals with analytical arteriovelocitogram (AVG). In the first part I a question of collection data AVG curve. Doppler effect and principal of Doppler‘s systems whose assistance data gain is analyzed in this semestral project. In the next part I focus on methods of analytic signals AVG. This means discribing the curve, calculating coefficients or methods for correct evaluation of the signal. These analyzes should categorize the measured data into groups according to pathological changes, of which doctors will determine the next course of treatment. The final chapter deals with the method of cluster analysis to classify data measured by Doppler systems. I deal with the algorithm method and choose the best clustering procedure. In the following section I concern with the practical part. I describe the data which were provided, the program selected for the analysis and the description of the alghoritm. The last chapter describes the results I’ve obtained.
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ECG signal classification
Smělý, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.
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