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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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Machine learning tools for Diagnosis of Heart Arrhythmia
Shkëmbi, Glejdis ; Vomlelová, Marta (advisor) ; Pilát, Martin (referee)
Title: Machine Learning Tools for Diagnosis of Heart Arrhythmia Author: Glejdis Shkëmbi Department / Institute: Department of Theoretical Computer Science and Mathematical Logic Supervisor of the bachelor thesis: Mgr. Marta Vomlelová, Ph.D., Department of Theoretical Computer Science and Mathematical Logic Abstract: Electrocardiogram (ECG) is considered to be the most reliable, efficient and low-cost tool used in the healthcare industry to diagnose cardiac arrhythmia. However, visual representation of ECG signals manually by medical workers is intricate and time-consuming, and may lead to human mistakes and inaccuracy in heartbeat recognition. In this paper, different machine learning techniques for the classification of five classes of ECG heartbeats using Discrete Wavelet Transform (DWT) features are compared. In particular, the significant role of statistical features of DWT coefficients in distinguishing between different heartbeat classes is highlighted. Performances of the models have been evaluated using the online MIT-BIH arrhythmia database. The obtained results indicate the reliability of the machine learning-based approaches for diagnoses of cardiac arrhythmia from ECG signals. Keywords: Electrocardiogram (ECG); Discrete Wavelet Transform (DWT); Support Vector Machine (SVM); Random Forest; Heart...
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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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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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