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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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Classification of microsleep by means of analysis EEG signal
Ronzhina, Marina ; Smital, Lukáš (referee) ; Čmiel, Vratislav (advisor)
This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.
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Fuzzy decision models
Starý, Josef ; Karpíšek, Zdeněk (referee) ; Žák, Libor (advisor)
This master's thesis is focused on fuzzy decision-making using fuzzy inference systems. In the first part, the math theory necessary in this field is described. The main objective of the thesis is to create a fuzzy inference system with adaptive cruise control function. Mathematical software MATLAB is used to accomplish this goal. The system evalutes situations and decides on the vehicle's response based on input variables such as the vehicles's speed, the speed of the previous vehicle, and the distance between vehicles. Its functionality is tested in this thesis using simulations of fictional scenarios, and then the system is compared to real adaptive cruise control systems using real data.
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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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Classification of microsleep by means of analysis EEG signal
Ronzhina, Marina ; Smital, Lukáš (referee) ; Čmiel, Vratislav (advisor)
This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.
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