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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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Mobile application for psychodiagnostics of preschool children
Guryča, Ondřej ; Smíšek, Radovan (referee) ; Škutková, Helena (advisor)
This bachelor thesis deals with the possibility of preschool children psychodiagnostics using a smartphone application. It is focused on the principle of psychodiagnostics and its different approach to preschool children psychodiagnostics, there are also described tests used in nowaday psychodiagnostics. In the next part is presented the current implementation of the OS Android application with chosen tests. At the end is an explanation of the way of evaluating data and recommendations for using the app.
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Automatic detection of myocardial infarction in ECG
Nejedlý, Lukáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
This master’s thesis deals with the automatic detection of myocardial infarction in ECG. Semester work consists of two parts. The theoretical part provides a description of the electrical conduction system of the heart, spreading of electrical activity through the heart muscle, the methods of ECG scanning and the ECG curve. There are also mentioned the causes of myocardial ischemia and various methods of its detection. Another part is devoted to high-frequency ECG, analysis of HFQRS and clinical studies which describe the use of high-frequency ECG in diagnosis of myocardial infarction. In the practical part is proposed an algorithm using low-frequency components ECG and an algorithm using high-frequency components ECG for automatic detection of myocardial infarction. The proposed algorithms are implemented in programming environment MATLAB and tested on signals from the PTB database. The final part of the master‘s thesis is devoted to the comparison of the success of myocardial infarction by means of low frequency and high frequency components of ECG and comparison of achieved results with results from clinical studies.
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Classification of free living data sensed with Faros
Šalamoun, Jan ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Topic of this master thesis is classification of free living data sensed with Faros. Faros is small compatible device which measure ECG and 3-axes accelerometric data. The first part of master thesis is find out how automatically measure free living activities by accelerometer and ECG. In next part was measured data of 8 activities from 10 probands. Automatic algorithms are made for this data in Matlab. This algorithms were used for this datasets and compare with manually recorded references. In the end of master thesis data were statistically evaluated.
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Recognition of vehicles using signals sensed by smartphone
Nevěčná, Leona ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Thanks to the development in recent years, the placement of miniaturized sensors such as accelerometers, gyroscopes, magnetometers, global positioning system receivers (GPS), microphones or others to commercially sold smartphones is increasing. Use of these sensors (which are to be found in the smartphone) for human activity recognition with health care improvement in mind is a discussed theme. Advantages of the use of smartphone for human movement monitoring lies in the fact that it is a device that the person measured carries with them and there are no additional costs. The disadvantages are a limited storage and battery. Therefore, only accelerometer, gyroscope, magnetometer, and microphone were chosen because their combination achieves best results. GPS sensor was excluded for its lack of reliability in sampling and for being energy demanding. Features were computed from the measured data and used for learning of the classification model. The highest accuracy was achieved with the use of a machine learning method called Random Forest. The main goal of this work was to create an algorithm for transportation mode recognition using signals sensed by a smartphone. The created algorithm succeeds in classification of walk, car, bus, tram, train, and bike in 97.4 % with 20 % holdout validation. When tested on a new set of data from the tenth volunteer, the resulting accuracy counted as average form classification recall for each transportation mode reached 90.49 %.
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ECG quality estimation
Vršková, Markéta ; Smíšek, Radovan (referee) ; Smital, Lukáš (advisor)
This master thesis solves the problem of estimating the quality of ECG signals. The main objective of the work is to implement a self-assessment of the quality assessment method based on the studied methods for estimating the quality of the ECG signal. The theoretical part of the thesis contains mainly the description of the electrical activity of the heart, cardiac anatomy, and physiology, electrocardiography, various types of ECG signal interference and methods describing the estimation of ECG signal quality. The practical part deals with the application of individual methods for estimating the quality of ECG signals. The SNR (signal-to-noise ratio) calculation is used to continuously estimate the quality of the ECG. Signal quality can also be judged based on statistical functions, adaptive filtering, or by analyzing independent components. The proposed method is based on the calculation of the correlation coefficient between the adaptive template and the disturbed signal. The robustness of the method was verified on artificially created ECG signals with different noise levels and then on real signals from the MIT-BIH database.
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Detection of true complete left bundle branch block
Opravilová, Kamila ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
The aim of this bachelor thesis is to get acquainted with the theory regarding electrophysiology of the heart and the pathology of Left Bundle Branch Block, LBBB for short. One of the goals is to make annotated database of QRS complexes for testing the LBBB algorithms. Next step will be to write and test these algorithms. Detection of LBBB is important, because it is one of the predictors of successfulness of resynchronization therapy. Conventional criteria for detection are not usable because of their low accuracy, that is why Straus' criteria had been made, those are way more accurate. This programme will abide these criteria. The overall evaluation of successfulness of this algorithm's detection is 100 % sensitivity and 69 % specifity. Therefore we can determine which patients do not suffer from LBBB without the risk of being wrong.
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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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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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