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Analysis of experimental ECG
Maršánová, Lucie ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.
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Identification of vertebrae type in CT data by machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Identification of vertebrae type by machine learning is an important task to facilitate the work of medical doctors. This task is embarrassed by many factors. First, a spinal CT imagining is usually performed on patiens with pathologies such as lesions, tumors, kyphosis, lordosis, scoliosis or patients with various implants that cause artifacts in the images. Furthermore, the neighboring vertebraes are very similar which also complicates this task. This paper deals with already segmented vertebrae classification into cervical, thoracic and lumbar groups. Support vector machines (SVM) and convolutional neural networks (CNN) AlexNet and VGG16 are used for classification. The results are compared in the conclusion.
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Reduction of Computation Cost in libSVM Using String Kernel Functions
Kubernát, Tomáš ; Sehnalová, Pavla (referee) ; Michlovský, Zbyněk (advisor)
The goal of this thesis was to implement four string functions into the library libSVM . Then apply series of tests with variable parameters values affecting the individual string functions using the library and string functions. Using the results of experiments the speed and success of clasification of my implementation of string functions in library libSVM was compared with the implementation of string functions in program kernels . In this thesis there are also described procedures of all tests along with measured data and their graphical representation.
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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Support of Mapping by Image Processing
Jaroš, Ján ; Herman, David (referee) ; Váňa, Jan (advisor)
This bachelor's thesis deals with methods of detection of selected objects in video and with importing these objects into OpenStreetMap central database based on their geographic location. It focuses mainly on recognition of road signs. First section briefly describes some of the most widely used methods and OpenStreetMap project itself. In the following chapters is given a more detailed overview of used methods of proposed system, its implementation and testing. The conclusion contains evaluation of whole work and the possible improvements are listed here.
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Comparison of Classification Methods
Dočekal, Martin ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam
Hrabinová, Barbora ; Sobek, Martin (referee) ; Menšík, Pavel (advisor)
The diploma thesis is focused on predictions of mean monthly flows for a purpose of control of storage functions when thinking differently positions of fictive reservoirs in the catchment area. One of the reservoir is situated in the upper part of the catchment area and the second is situated in the middle part of catchment area. Predictions are made by Support vector machine method in RStudio and with the use of R language. Predicted values of flows was evaluated by the correlation coefficient, coefficient of determination, Root mean square error and than was made the simulation of operation of storage function, which was evaluated by Total sum of squares modificated for problems of water management. In the end was made a comparison of both of the reservoirs for assessment of the suitability of the method.
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Detection of atrial fibrillation in short-term ECG
Ambrožová, Monika ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).
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Using of Data Mining Method for Analysis of Social Networks
Novosad, Andrej ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
Thesis discusses data mining the social media. It gives an introduction about the topic of data mining and possible mining methods. Thesis also explores social media and social networks, what are they able to offer and what problems do they bring. Three different APIs of three social networking sites are examined with their opportunities they provide for data mining. Techniques of text mining and document classification are explored. An implementation of a web application that mines data from social site Twitter using the algorithm SVM is being described. Implemented application is classifying tweets based on their text where classes represent tweets' continents of origin. Several experiments executed both in RapidMiner software and in implemented web application are then proposed and their results examined.
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Biologically Inspired Methods of Object Recognition
Vaľko, Tomáš ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
Object recognition is one of many tasks in which the computer is still behind the human. Therefore, development in this area takes inspiration from nature and especially from the function of the human brain. This work focuses on object recognition based on extracting relevant information from images, features. Features are obtained in a similar way as the human brain processes visual stimuli. Subsequently, these features are used to train classifiers for object recognition (e.g. SVM, k-NN, ANN). This work examines the feature extraction stage. Its aim is to improve the feature extraction and thereby increase performance of object recognition by computer.
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