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HRV analysis in the context of daily activities
Indrák, Václav ; Smital, Lukáš (referee) ; Novotná, Petra (advisor)
The aim of this bachelors thesis is to measure ECG recordings on voulenteers, and following analysis of HRV from these recordings. It persues the explanation of basic metrics used to evaluate HRV, used both in clinical and scientific practice and their following programming implementation in Matlab environment to achieve the most accurate results possible, which are than assessed.
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ECG based atrial fibrillation detection
Plch, Vít ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals with Bayes and Lavenberg-Marquardt neural networks. Bayes neural network with 23 neurons in hidden layer is best with F1 measure = 83,6 %, Sensitivity = 88,1 % and Specificity 94,5 %.
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ECG based atrial fibrillation detection
Plch, Vít ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals.
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Automatic Analysis of Heart Rate Variability Signals
Kubičková, Alena ; Halámek, Josef (referee) ; Lhotská, Lenka (referee) ; Kozumplík, Jiří (advisor)
This dissertation thesis is dedicated to the heart rate variability and methods of its evaluation. It mainly focuses on nonlinear methods and especially on the Poincaré plot. First it deals with the principle and nature of the heart rate variability, then the ways of its representation, linear and also nonlinear methods of its analysis and physiological and pathophysiological influence on heart rate variability changes. In particular, there is emphasis on the metabolic syndrome. In the next section of the thesis there are compared and evaluated different ways of representation of the heart rate variability and further are tested selected methods of heart rate variability analysis on unique data from patients with the metabolic syndrome and healthy subjects provided by the Institute of Scientific Instruments, Academy of Sciences of Czech Republic. In particular, they are used the Poincaré plot and its parameters SD1 and SD2, commonly used time domain and frequency domain parameters, parameters evaluating signal entropy and the Lyapunov exponent. SD1 and SD2 combining the advantages of time and frequency domain methods of heart rate variability analysis distinguish successfully between patients with the metabolic syndrome and healthy subjects.
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|
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HRV analysis in the context of daily activities
Indrák, Václav ; Smital, Lukáš (referee) ; Novotná, Petra (advisor)
The aim of this bachelors thesis is to measure ECG recordings on voulenteers, and following analysis of HRV from these recordings. It persues the explanation of basic metrics used to evaluate HRV, used both in clinical and scientific practice and their following programming implementation in Matlab environment to achieve the most accurate results possible, which are than assessed.
|
|
|
ECG based atrial fibrillation detection
Plch, Vít ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals with Bayes and Lavenberg-Marquardt neural networks. Bayes neural network with 23 neurons in hidden layer is best with F1 measure = 83,6 %, Sensitivity = 88,1 % and Specificity 94,5 %.
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|
|
ECG based atrial fibrillation detection
Plch, Vít ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals.
|
|
|
Automatic Analysis of Heart Rate Variability Signals
Kubičková, Alena ; Halámek, Josef (referee) ; Lhotská, Lenka (referee) ; Kozumplík, Jiří (advisor)
This dissertation thesis is dedicated to the heart rate variability and methods of its evaluation. It mainly focuses on nonlinear methods and especially on the Poincaré plot. First it deals with the principle and nature of the heart rate variability, then the ways of its representation, linear and also nonlinear methods of its analysis and physiological and pathophysiological influence on heart rate variability changes. In particular, there is emphasis on the metabolic syndrome. In the next section of the thesis there are compared and evaluated different ways of representation of the heart rate variability and further are tested selected methods of heart rate variability analysis on unique data from patients with the metabolic syndrome and healthy subjects provided by the Institute of Scientific Instruments, Academy of Sciences of Czech Republic. In particular, they are used the Poincaré plot and its parameters SD1 and SD2, commonly used time domain and frequency domain parameters, parameters evaluating signal entropy and the Lyapunov exponent. SD1 and SD2 combining the advantages of time and frequency domain methods of heart rate variability analysis distinguish successfully between patients with the metabolic syndrome and healthy subjects.
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