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Classification of free living data
Rychtárik, Martin ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
The topic of this bachelor thesis is classification of free living data, captured by the accelerometer sensor of a smart phone. The first part of the thesis deals with the possibilities of recording daily activity using accelerometer and subsequent classification by neural network. In the next section, the data of eight different daily activities were recorded on ten people. An algorithm containing a neural network was created for the data in the MATLAB programming environment to automatically identify the activities. In the last part of the work the algorithm classification was compared with manually recorded reference and the results were statistically evaluated.
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Automatic stress detection using non-EEG biological signals
Malina, Ondřej ; Kolářová, Jana (referee) ; Smíšek, Radovan (advisor)
This work deals with the problem of stress detection using non-EEG biosignals. The first part deals with the definition of stress and related concepts. Describes possible views of the phenomenon of stress, mentions possible causes of stress, as well as physiological and psychological manifestations of short and long-term effects of stress. In addition, this work deals with several different methods used to detect stress with non-EEG signals. For this purpose, a short search of articles dealing with this topic is available in this paper. The last chapter of this work describes the algorithm design using the c-mean fuzzy method for detecting stress values in data obtained form five different non-EEG signals.
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Detection of paroxysmal atrial fibrillation and atrial flutter
Krmela, Jan ; Němcová, Andrea (referee) ; Smíšek, Radovan (advisor)
This bachelor thesis deals with the problem of atrial fibrillation and flutter, the pathophysiology of these arrhythmias and their automatic detection. It includes a theoretical introduction necessary to understand the basal anatomy of the heart, its function, the origin and description of the electrocardiogram and a chapter on cardiac arrhythmias. It also includes a review of automatic detection of atrial fibrillation. The databases used in the practical part of the thesis are also described. The implementation of heart rhythm classification and automatic detection of the beginning and end of paroxysmal episodes is performed in MATLAB environment, the proposed algorithm is tested on the described databases and the results are evaluated.
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Heart beat representation for classification
Smíšek, Radovan ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Selection of ECG segment plays a significant role in design of a heart beat classifier. The type of selected segments influences the classification not only in regard to the type and maximum number of recognized pathological groups but also in regard to the complexity of classification model, which consequently creates indirect demands on the memory of the computer technology used as well as on the time needed for the classification. The thesis is focused on the comparison of success rates of the ECG heart beat classifications in different input segments. The input segments used were QRST, RST, ST-T, QRS, and T. The ECG signal was obtained from isolated rabbit hearts and divided into individual types according to the T-wave amplitude and changes in the ST segment. The signal subsequently enters the artificial neural network where it is classified into predefined types. The network used had twenty-four neurons in the first layer and one neuron in the second layer. Efficiency of the classification is in the conclusion of this thesis.
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ECG quality estimation
Pospíšil, Jan ; Smíšek, Radovan (referee) ; Smital, Lukáš (advisor)
This bachelor thesis deals with the question of estimation of the quality of the ECG signals, which is a key parameter for determining the diagnosis. The theoretical part deals with the basic knowledge concerning cardiac physiology, electrocardiography and finally the types of interferences that can occur during the measurement. The following practical part will deal with the published methods and the proposal of methods for estimating signal quality and their testing on artificial and real data.
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Deep learning based QRS delineator
Malina, Ondřej ; Ronzhina, Marina (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Muscle noise filtering in ECG signals
Fedorov, Vasilii ; Smíšek, Radovan (referee) ; Smital, Lukáš (advisor)
This work deals with problematic of muscle noise filtration in ECG signals. It contains theoretical and practical parts. In theoretical part we first mentioned a topicality of ECG scanning and filtration. Then we got acquainted with the origin of ECG, it's properties, and types of noises, that typically occurring there. Further different known methods of linear and non-linear techniques in EMG filtration were discussed. After we got acquainted with wavelet transform and its possibilities practical part was carried out in environment MATLAB 2020b®. Wiener wavelet filter was implemented and supplemented by a threshold adaptive function. Parameters were optimized with brute force method in reduced range. The evaluation of the filter took place on a CSE database, where the results were compared with the authors of other methods. In result the filter shows good filtration capabilities and stability.
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Removal of pacing spikes from the electrocardiographic signal
Smíšek, Radovan ; Plešinger,, Filip (referee) ; Postránecká, Tereza (advisor)
The goal of this thesis is to detect pacing pulses in ultra high-frequency ECG so as to remove these pacing pulses. It makes evaluation of higher frequency components of QRS complex possible. This evaluation is impossible while pacing pulses are present. Chosen issue is solved using heuristic algorithm. Algorithm uses spacing of signal by line in the area which is not influenced by pacing pulses. Subsequently this line is made longer and using differences between line and signal (or another rules) edges of pacing pulses are detected. The top of the stimulation tip is detected by thresholding envelope of original signal´s first difference. More algorithms are tested in this thesis. Several methods of removing pacing pulses are suggested in thesis. Envelopes of high-frequency components are created. Envelopes are analyzed subsequently and suggested methods of removing pacing pulses are compared on the basis of these analysis. Finally the detection efficiency is evaluated.
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