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Clustering of ECG cycles
Němečková, Karolína ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
This bachelor thesis deals with application of cluster analysis to different ECG records in order to identify particular cardiac pathologies. The work is mainly focused on the detection of premature atrial and premature ventricular beats. Presented approach is based on the signal correlation and further beat type identification and beats clustering via specific ECG features. By evaluation the method on test data, we obtained TPR 73.40 %, FPR 91.00 %, PPV 29.00 %, ACC 90.00 %, F1 41.40 % for PAC detection and TPR 76.50 %, FPR 94.20 %, PPV 45.90 %, ACC 93.10 %, F1 57.40 % for PVC detection. Pure F1 and PPV is due to high number of false positive detections mainly in noisy ECG or ECG with manifested atrial fibrillation.
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Detection of selected cardiac arrhythmias in ECG
Němečková, Karolína ; Ředina, Richard (referee) ; Ronzhina, Marina (advisor)
This thesis deals with classification of ECG records focusing on less classifiable arrhythmia (atrial flutter, atriventricular block I. and II. degree). In the theoretical part of the thesis deep learning used in classification of ECG records with a focus on the convolutional neural networks are described. The database of ECG records with a brief description of detected arrhythmias is further described. The practical part implements the proposed convolutional neural network in the Python environment. The evaluation of the arrhythmia detection quality was done using mainly the F1 score. The results were discussed at the end of the thesis.
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Detection of selected cardiac arrhythmias in ECG
Němečková, Karolína ; Ředina, Richard (referee) ; Ronzhina, Marina (advisor)
This thesis deals with classification of ECG records focusing on less classifiable arrhythmia (atrial flutter, atriventricular block I. and II. degree). In the theoretical part of the thesis deep learning used in classification of ECG records with a focus on the convolutional neural networks are described. The database of ECG records with a brief description of detected arrhythmias is further described. The practical part implements the proposed convolutional neural network in the Python environment. The evaluation of the arrhythmia detection quality was done using mainly the F1 score. The results were discussed at the end of the thesis.
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Clustering Of Ecg Cycles
Němečková, Karolína
The paper deals with application of cluster analysis to different ECG records in order to identify particular cardiac pathologies. The work is mainly focused on the detection of premature atrial and premature ventricular beats. Presented approach is based on the signal correlation and further beat type identification and beats clustering via specific ECG features and detection rules, including fuzzy expert rules. By evaluation the method on test data, we obtained Se 76,0 %, Sp 90,2 %, F1 43,8 %, Acc 89,5 %, and PPV 31,1 %. Pure F1 and PPV is due to high number of false positive detections mainly in noisy ECG or ECG with manifested atrial fibrillation.
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Clustering of ECG cycles
Němečková, Karolína ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
This bachelor thesis deals with application of cluster analysis to different ECG records in order to identify particular cardiac pathologies. The work is mainly focused on the detection of premature atrial and premature ventricular beats. Presented approach is based on the signal correlation and further beat type identification and beats clustering via specific ECG features. By evaluation the method on test data, we obtained TPR 73.40 %, FPR 91.00 %, PPV 29.00 %, ACC 90.00 %, F1 41.40 % for PAC detection and TPR 76.50 %, FPR 94.20 %, PPV 45.90 %, ACC 93.10 %, F1 57.40 % for PVC detection. Pure F1 and PPV is due to high number of false positive detections mainly in noisy ECG or ECG with manifested atrial fibrillation.
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Possibilities of the Utilization of Teambuilding
Němečková, Karolína ; Vinšová, Tereza (advisor) ; Pavlis, Jan (referee)
The aim of this thesis is to find out the situation of use of teambuilding in the chosen organizations and this situation sum up and pertinently recommend possible improvement. Questionnaires are formulated by myself serve to obtain data. Resources to work are answers of employees, for who is teambuilding set, but also for employers, pertinently headquarters. From my diploma thesis, I expect that I find, if teambuilding is efficient in each organization, is correct used and bring results, and also a satisfaction of employees , but also employers.
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The causes of poor learning results in the subject 6MI211 - Data Analysis
Němečková, Karolína ; Komárková, Lenka (advisor) ; Voráček, Jan (referee)
The aim of this bachelor thesis is to statistically process data related to the failure of full time students in the subject Data Analysis and to appropriately interpret the acquired results. Resources to our work are the academic results based on Data Analysis and Math for economists from the winter semester of the academic year 2006/2007 and 2007/2008 and, in particular, a questionnaire formulated by ourselves. The nature of the data collected leads to the analysis of qualitative variables. First single data are described using a table of relative and absolute frequencies for each variable. In addition, we have worked with selected pairs of variables, namely the two-data which are processed in the form of contingency tables and these tables are then further analyzed. There are, inter alia, used tests of independence and homogeneity. For better understanding we have used the tables and the related tests are completed by graphs. From our bachelor thesis, we expect that we find the views of students on this subject and find out, why this subject causes so many problems for most of them.
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