National Repository of Grey Literature 34 records found  previous11 - 20nextend  jump to record: Search took 0.01 seconds. 
Automatic detection of heart pathologies using high-frequency components of QRS complex
Daňová, Ľudmila ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
The aim of this thesis is to analyse high-frequency ECG to detect some heart diseases. This is performed with averaging of selected QRS complexes for each lead of the signal; these are thenfilteredin range 500-1 000 Hz. After that the envelope of the signal is done and here the peaks are detected. Based on mutual positions of this peaks, it is possible to detectwhat kind od signal we treat.
Detection Of P Wave During Second-Degree Atrioventricular Block In Ecg Signals
Maršánová, Lucie ; Němcová, Andrea ; Smíšek, Radovan
Automatic detection of P wave during the second-degree AV block is the main condition for automatic detection of this pathology. This work deals with developing of the algorithm for P wave detection. The algorithm is appropriate for ECG signals with AV block as well as signals with other rhythm types (it does not produce false positive P wave detections). For P wave detection, the phasor transform is applied and several innovative rules are created. These rules are based on knowledge of heart manifestation during both physiological and pathological heart function. The proposed algorithm consists of four parts – filtration, QRS complex detection, application of rules, and P wave detection. The accuracy of the P wave detection algorithm is 99.74 % for signals with AV block, and 99.82 % for signals without any pathologies.
Automatic detection of stress using biological signals
Votýpka, Tomáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
Bachelor's thesis is focused on stress detection. This thesis defines the concept of stress, analyzes the appropriate biological signals for stress detection, presents databases of biological signals, that were used for stress detection and mentions methods of automatic stress detection. Then, a stress detection program was implemented in the MATLAB software environment. A freely available database of non-EEG signals was used to implement the program. Models classifying stress were created using 4 machine learning methods for binary classification and 3 machine learning methods for classifying 4 psychical states. Efficiency of the classification was summarized in the conclusion of this thesis.
Fetal ECG records analysis
Hláčiková, Michaela ; Smíšek, Radovan (referee) ; Smital, Lukáš (advisor)
This thesis is focused on the analysis of fetal ECG records measured by indirect method from mother´s abdomen. The thesis consists of the theoretical part is focused on fetal, heart development and description of fetal ECG signal. This thesis also offers an overview of fECG signal processing methods used nowadays. The practical part of the thesis deals with the implementation of algorithms based on wavelet transformation and Least Mean Square LMS method in Matlab programming environment. The final part of the thesis consists of the analysis of achieved results.
Clustering of ECG cycles
Ředina, Richard ; Smíšek, Radovan (referee) ; Ronzhina, Marina (advisor)
The bachelor thesis explores the aplication of cluster analysis on diferent ECGs in order to create a reliable algorithm for detecting different QRS complexes. Algorithm comprises filtration, R-wave positions adjustment, model cycle creation and comparasion based on mean square error and correlation. Both, correlation and mean square error, become data for k-means clustering. The number of clusters is derived from silhouette values for diferent numbers of clusters.
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.
Comparison of heart activity sensing devices
Babicová, Martina ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
The goal of this work is comparison of heart activity sensing devices. However, an ECG record cannot be evaluated with the presence of muscle interference. Removing this noise is one of the needs for device success. The theoretical part represents electrophysiology of the heart, electrocardiography, various interferences types, theoretical basis for recording of biosignals including used devices and methods signal quality estimation. The practical part is SNR (signal-to-noise ratio) calculation. The Wavelet filter and Wiener filter-based wavelet domain are used to separate the useful and noise component.
ECG signal quality annotation
Waloszek, Vojtěch ; Smíšek, Radovan (referee) ; Vítek, Martin (advisor)
This thesis gives basic information summary about electrophysiology of heart and electrocardiography and overview of several signal quality assessment methods. It also presents a new method for evaluating ECG quality, shows how signal quality indices are extracted and how the quality annotation is performed. It also gives test results of how the signal quality indices reflect the presence of corresponding noise and whether the quality annotation is correct.
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.
Automatic detection of strict left bundle branch block
Němčáková, Jesika ; Ronzhina, Marina (referee) ; Smíšek, Radovan (advisor)
The aim of this paper is to introduce the theory behind electrophysiology of hearth and pathology of left bundle branch. Furthermore, algorithms for automatic detection of left bundle branch block (LBBB) according to strict criteria are proposed. Algorithms are tested based on data from THEW databes within the Matlab interface. The final part of the bachelor’s thesis is devoted to the evaluation of the success of individual algorithms and the comparison of results on training data with test data.

National Repository of Grey Literature : 34 records found   previous11 - 20nextend  jump to record:
See also: similar author names
1 SMÍŠEK, Rostislav
1 Smíšek, R.
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