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QRS detection based on Stockwell transform
Kašík, Ondřej ; Kozumplík, Jiří (referee) ; Smital, Lukáš (advisor)
This bachelor´s thesis deals with the detection of QRS complexes in ECG record. The thesis provides a brief information related to the heart anatomy, generation of electrical signals in the heart, recording and description of the ECG record. In more detail, there is a description of the detection of QRS complexes by various methods and realization of a detector based on Stockwell transform, Shannon energy and adaptive thresholding. The evaluation process of the detection efficiency is also included. Sensitivity and positive prediction of the proposed detector on the complete MIT-BIH Arrhythmia database reached 99.80 % and 99.88 % respectively.
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Detection of QRS complexes in ECG signals
Zhorný, Lukáš ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This thesis deals with the detection of QRS complexes from electrocardiograms using time-frequency analysis. Detection procedures are based on wavelet and Stockwell transform. The theoretical part describes the basics of electrocardiography, then introduces common approaches to time-frequency analysis, such as short-time Fourier transform (STFT), wavelet transform and Stockwell transform. These algorithms were tested on a set of electrograms from the MIT-BIH and CSE-MO1 arrhythmia database. For the CSE database worked best the method based on the wavelet transform with the filter bank Symlet4, with the resulting value of sensitivity 100 % and positive predictivity 99.86%. For the MIT database had the best performance the detector using the Stockwell transform with values of sensitivity 99.54% and positive predictivity 99.68%. The results were compared with the values of other authors mentioned in the text.
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Detection of K-complexes in sleep EEG signals
Hlaváčová, Kristýna ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This master’s thesis deals with issues of the detection of K-complexes in EEG sleep signals. Record from an electroencephalograph is important for non-invasive diagnosis and research of brain activity. The scanned signal is used to examine sleep phases, disturbances, states of consciousness and the effects of various substances. This work follows the automatic detection of K-complexes, because the manual labeling of graphoelements is complicated. Two approaches were used –Stockwell transform and bandpass filtration followed by TKEO operator application. All algorithms were created in the MATLAB R2014a.
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Detection of QRS complexes in ECG signals
Zhorný, Lukáš ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This thesis deals with the detection of QRS complexes from electrocardiograms using time-frequency analysis. Detection procedures are based on wavelet and Stockwell transform. The theoretical part describes the basics of electrocardiography, then introduces common approaches to time-frequency analysis, such as short-time Fourier transform (STFT), wavelet transform and Stockwell transform. These algorithms were tested on a set of electrograms from the MIT-BIH and CSE-MO1 arrhythmia database. For the CSE database worked best the method based on the wavelet transform with the filter bank Symlet4, with the resulting value of sensitivity 100 % and positive predictivity 99.86%. For the MIT database had the best performance the detector using the Stockwell transform with values of sensitivity 99.54% and positive predictivity 99.68%. The results were compared with the values of other authors mentioned in the text.
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Detection of K-complexes in sleep EEG signals
Hlaváčová, Kristýna ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This master’s thesis deals with issues of the detection of K-complexes in EEG sleep signals. Record from an electroencephalograph is important for non-invasive diagnosis and research of brain activity. The scanned signal is used to examine sleep phases, disturbances, states of consciousness and the effects of various substances. This work follows the automatic detection of K-complexes, because the manual labeling of graphoelements is complicated. Two approaches were used –Stockwell transform and bandpass filtration followed by TKEO operator application. All algorithms were created in the MATLAB R2014a.
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QRS detection based on Stockwell transform
Kašík, Ondřej ; Kozumplík, Jiří (referee) ; Smital, Lukáš (advisor)
This bachelor´s thesis deals with the detection of QRS complexes in ECG record. The thesis provides a brief information related to the heart anatomy, generation of electrical signals in the heart, recording and description of the ECG record. In more detail, there is a description of the detection of QRS complexes by various methods and realization of a detector based on Stockwell transform, Shannon energy and adaptive thresholding. The evaluation process of the detection efficiency is also included. Sensitivity and positive prediction of the proposed detector on the complete MIT-BIH Arrhythmia database reached 99.80 % and 99.88 % respectively.
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