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ECG arrhythmia detection
Pchálková, Aneta ; Filipenská, Marina (referee) ; Novotná, Petra (advisor)
This thesis describes the principles of ECG, the physiology of arrhythmias, their origin, and manifestations in the ECG, focusing on ventricular extrasystoles and bundle branch blocks. It examines contemporary methods for detecting these arrhythmias and acquiring the necessary features for their implementation. The work also covers data handling, including data preprocessing. Classification of ventricular extrasystoles and bundle branch blocks is implemented using k-nearest neighbors models.
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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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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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