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An Arrhytmia Classifiication Based on RR Interval
Novotný, Jiří ; Škutková, Helena (referee) ; Kozumplík, Jiří (advisor)
This bachelor’s thesis deals with automatic classification of cardiac rhytm based on the sequence of RR intervals. The theoretical part deals both with characteristics of arrhytmias, which have been classified and the principles of the detectors and classificators used in this thesis. In the practical part, the QRS detector based on the calculation of the envelope of the ECG signal was realized in Matlab. Furthermore, the algoritm classifying arrhytmias based on the analysis of RR intervals was created. The reliability of the detector and the efficiency of the classificator was tested on the standard database CSE. The sensitivity of the detector was 98,28% and its positive predictive value was 98,38%. The diagnoses of the implemented classificator concurred in 73,17% with the cardiologists‘ diagnoses.
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Detection of premature ventricular contractions in ECG
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of extrasystoles from ECG and description of electrocardiogram, cardiac conduction system, extrasystoles and ventricular tachycardia. Extrasystoles are premature ventricular contraction caused by ectopic heartbeats. Classification is based on signal preprocessing, detection of R peak, the heartbeat segmentation, the feature description methods, normalization of features and the learning algorithms used. Selected and realized methods achieved classification accuracy ACC = 98 %, sensitivity SE = 100 % and specificity SP = 96,1 %. Gained features are also used for detection bundle branch block.
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Diagnosis of Ventricular Tachycardias from Electrocardiogram
Šrutová, Martina ; Hrubeš, Jan (referee) ; Kolářová, Jana (advisor)
The aim of this thesis is a diagnosis of ventricular tachycardias, fibrillations and flutters from electrocardiogram. These disturbances of heart rate are ranked among the life threatening arrhytmias. This work presents own method of the automatic detection, which is created for the ECG holter monitoring system. The proposed algorithm is based on the detection in the spectral domain, which is supported by the detection in the time domain. The results show the discrimination of arrhytmias from the normal sinus rhythm and the discrimination from the noise. The method is tested with ECG records from the The AHA Database (American Heart Association) and from The MIT-BIH Malignant Ventricular Arrhythmia Database.
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Detection of premature ventricular contractions in ECG
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of extrasystoles from ECG and description of electrocardiogram, cardiac conduction system, extrasystoles and ventricular tachycardia. Extrasystoles are premature ventricular contraction caused by ectopic heartbeats. Classification is based on signal preprocessing, detection of R peak, the heartbeat segmentation, the feature description methods, normalization of features and the learning algorithms used. Selected and realized methods achieved classification accuracy ACC = 98 %, sensitivity SE = 100 % and specificity SP = 96,1 %. Gained features are also used for detection bundle branch block.
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An Arrhytmia Classifiication Based on RR Interval
Novotný, Jiří ; Škutková, Helena (referee) ; Kozumplík, Jiří (advisor)
This bachelor’s thesis deals with automatic classification of cardiac rhytm based on the sequence of RR intervals. The theoretical part deals both with characteristics of arrhytmias, which have been classified and the principles of the detectors and classificators used in this thesis. In the practical part, the QRS detector based on the calculation of the envelope of the ECG signal was realized in Matlab. Furthermore, the algoritm classifying arrhytmias based on the analysis of RR intervals was created. The reliability of the detector and the efficiency of the classificator was tested on the standard database CSE. The sensitivity of the detector was 98,28% and its positive predictive value was 98,38%. The diagnoses of the implemented classificator concurred in 73,17% with the cardiologists‘ diagnoses.
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Diagnosis of Ventricular Tachycardias from Electrocardiogram
Šrutová, Martina ; Hrubeš, Jan (referee) ; Kolářová, Jana (advisor)
The aim of this thesis is a diagnosis of ventricular tachycardias, fibrillations and flutters from electrocardiogram. These disturbances of heart rate are ranked among the life threatening arrhytmias. This work presents own method of the automatic detection, which is created for the ECG holter monitoring system. The proposed algorithm is based on the detection in the spectral domain, which is supported by the detection in the time domain. The results show the discrimination of arrhytmias from the normal sinus rhythm and the discrimination from the noise. The method is tested with ECG records from the The AHA Database (American Heart Association) and from The MIT-BIH Malignant Ventricular Arrhythmia Database.
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