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METHODS FOR DETECTION AND CLASSIFICATION IN ECG ANALYSIS
 Kičmerová, Dina ; Kremláček,, Jan (oponent) ; Sovka, Pavel (oponent) ; Provazník, Ivo (vedoucí práce)
The first part of the presented work is focused on measuring of QT intervals. QT interval can be an indicator of the cardiovascular health of the patient and detect any potential abnormalities. The QT interval is measured from the onset of the QRS complex to the end of the T wave. However, measurements for the end of the T wave are often highly subjective and the corresponding verification is difficult. Here we propose two methods of QT interval measuring - wavelet based and template matching method. Methods are compared with each other and tested on standard QT database. The second part of the presented work is focused on modelling of arrhythmias using McSharry’s model followed with classification using an artificial neural network. The proposed method uses pre-processing of signals with Linear Approximation Distance Thresholding method and Line Segment Clustering method for establishing of initial parameters of McSharry’s model. The ECG data is taken from standard MIT/BIH arrhythmia database. The modelling was tested on the whole MIT arrhythmia database signals, lead MLII (modified limb lead II). All signals could be modelled with 10 Gaussians functions without significant distortion. The third part of the presented work is focused on ECG classification. Premature Ventricular Contraction (PVC) beats are of crucial importance in evaluating and predicting life threatening ventricular arrhythmias. An algorithm is proposed for the identification of PVC beats. Signals modelled with 30 Gaussians parameters were supplied to the input of artificial neural network. Multilayer perceptron was used with classification accuracy of 93.10% for premature ventricular contraction (PVC) and 96.43% for normal beats.
Úplný záznam
METHODS FOR DETECTION AND CLASSIFICATION IN ECG ANALYSIS
 Kičmerová, Dina ; Kremláček,, Jan (oponent) ; Sovka, Pavel (oponent) ; Provazník, Ivo (vedoucí práce)
The first part of the presented work is focused on measuring of QT intervals. QT interval can be an indicator of the cardiovascular health of the patient and detect any potential abnormalities. The QT interval is measured from the onset of the QRS complex to the end of the T wave. However, measurements for the end of the T wave are often highly subjective and the corresponding verification is difficult. Here we propose two methods of QT interval measuring - wavelet based and template matching method. Methods are compared with each other and tested on standard QT database. The second part of the presented work is focused on modelling of arrhythmias using McSharry’s model followed with classification using an artificial neural network. The proposed method uses pre-processing of signals with Linear Approximation Distance Thresholding method and Line Segment Clustering method for establishing of initial parameters of McSharry’s model. The ECG data is taken from standard MIT/BIH arrhythmia database. The modelling was tested on the whole MIT arrhythmia database signals, lead MLII (modified limb lead II). All signals could be modelled with 10 Gaussians functions without significant distortion. The third part of the presented work is focused on ECG classification. Premature Ventricular Contraction (PVC) beats are of crucial importance in evaluating and predicting life threatening ventricular arrhythmias. An algorithm is proposed for the identification of PVC beats. Signals modelled with 30 Gaussians parameters were supplied to the input of artificial neural network. Multilayer perceptron was used with classification accuracy of 93.10% for premature ventricular contraction (PVC) and 96.43% for normal beats.
Úplný záznam

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