2017

VČELÁK, P., KRYL, M., RAČÁK, L., KLEČKOVÁ, J. Acquisition of Confidential Patient Data Over Shared Mobile Device. In BIOSTEC2017 ? 10th International Joint Conference on Biomedical Engineering Systems and Technologies. Setúbal: ScitePress, 2017. p. 334-339. ISBN 978-989-758-213-4
Abstract: Mobile devices have already been designed for many applications. Smartphones and tablet computers are modern, widespread and affordable solutions used for various purposes. Nowadays mobile devices are widely used in telemedicine. It is usually assumed, that the device is owned and used by a single person. We focus on security concerns and constraints from a different point of view ? when the device is shared. In this paper, we are proposing a novel approach to prevent leakage of patient's confidential data when the device is used by multiple patients at the hospital's clinic or department. We present a prototype application and discuss its use case and designed workflow

VAŘEKA, L., PROKOP, T., MOUČEK, R., MAUTNER, P., ŠTĚBETÁK, J. Application of Stacked Autoencoders to P300 Experimental Data. In Artificial Intelligence and Soft Computing. Cham: Springer, 2017. p. 187-198. ISBN 978-3-319-59062-2 , ISSN: 0302-9743
Abstract: Deep learning has emerged as a new branch of machine learning in recent years. Some of the related algorithms have been reported to beat state-of-the-art approaches in many applications. The main aim of this paper is to verify one of the deep learning algorithms, specifically a stacked autoencoder, to detect the P300 component. This component, as a specific brain response, is widely used in the systems based on brain-computer interface. A simple brain-computer interface experiment more than 200 school-age participants was performed to obtain large datasets containing the P300 component. After feature extraction the collected data were split into the training and testing sets. State-of-the art BCI classifiers (such as LDA, SVM, or Bayesian LDA) were applied to the data and then compared with the results of stacked autoencoders.

PAPEŽ, V., MOUČEK, R. Applying an Archetype-Based Approach to Electroencephalography/Event-Related Potential Experiments in the EEGBase Resource. Frontiers in Neuroinformatics, 2017, Volume 11, Issue 24, p. 1-13. ISSN: 1662-5196
Abstract: The purpose of this study is to investigate the feasibility of applying openEHR (an archetype-based approach for electronic health records representation) to modeling data stored in EEGBase, a portal for experimental electroencephalography/eventrelated potential (EEG/ERP) data management. The study evaluates re-usage of existing openEHR archetypes and proposes a set of new archetypes together with the openEHR templates covering the domain. The main goals of the study are to (i) link existing EEGBase data/metadata and openEHR archetype structures and (ii) propose a new openEHR archetype set describing the EEG/ERP domain since this set of archetypes currently does not exist in public repositories

JEŽEK, P., MOUČEK, R. Data Format for Storing ANT Sensors Data. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies. Setúbal: ScitePress, 2017. p. 396-400. ISBN 978-989-758-213-4
Abstract: Medical treatment of sudden and especially chronic diseases has become more expensive. People suffering from a variety of diseases had been traditionally treated in hospitals for a long time. Fortunately, the current situation has been changing also thanks to relatively cheap body sensors and development of systems for home treatment. It brings inconsiderable cost savings and improves patients? comfort. On the other hand, it puts demands on the used technical infrastructure and home treatment system developers who must solve integration of different systems. A crucial point is a definition of unified data formats facilitating transfer and storage of data to/in remote databases. There are standards and APIs such as Zigbee, Bluetooth low energy or ANT+ that define a protocol for data transfer. However, they do not define a suitable format for long term data storing. In this paper, data coming from ANT+ sensors have been studied and metadata related to all kinds of body sensors and raw data and metadata specific to individual sensors have been defined. Then a framework organizing data and metadata obtained from ANT+ sensors into an open and general data format suitable for long term storage of sensor data is introduced. Finally, a sample use-case showing the transfer of data from a sensor into a data storage is presented.

VAŘEKA, L., BRŮHA, P., MOUČEK, R., MAUTNER, P., ČEPIČKA, L., HOLEČKOVÁ, I. Development coordination disorder in children ? experimental work and data annotation. GigaScience, 2017, Volume 6, Issue 4, p. 1-6. ISSN: 2047-217X
Abstract: Developmental coordination disorder (DCD) is described as a motor skill disorder characterized by a marked impairment in the development of motor coordination abilities that significantly interferes with performance of daily activities and/or academic achievement. Since some electrophysiological studies suggest differences between children with/without motor development problems, we prepared an experimental protocol and performed electrophysiological experiments with the aim of making a step toward a possible diagnosis of this disorder using the event-related potentials (ERP) technique. The second aim is to properly annotate the obtained raw data with relevant metadata and promote their long-term sustainability. Results: The data from 32 school children (16 with possible DCD and 16 in the control group) were collected. Each dataset contains raw electroencephalography (EEG) data in the BrainVision format and provides sufficient metadata (such as age, gender, results of the motor test, and hearing thresholds) to allow other researchers to perform analysis. For each experiment, the percentage of ERP trials damaged by blinking artifacts was estimated. Furthermore, ERP trials were averaged across different participants and conditions, and the resulting plots are included in the manuscript. This should help researchers to estimate the usability of individual datasets for analysis. Conclusions: The aim of the whole project is to find out if it is possible to make any conclusions about DCD from EEG data obtained. For the purpose of further analysis, the data were collected and annotated respecting the current outcomes of the International Neuroinformatics Coordinating Facility Program on Standards for Data Sharing, the Task Force on Electrophysiology, and the group developing the Ontology for Experimental Neurophysiology. The data with metadata are stored in the EEG/ERP Portal.

MOUČEK, R., VAŘEKA, L., PROKOP, T., ŠTĚBETÁK, J., BRŮHA, P. Event-related potential data from a guess the number brain-computer interface experiment on school children. Scientific Data, 2017, Volume 4, Issue March 2017, p. 1-11. ISSN: 2052-4463
Abstract: Guess the number is a simple P300-based brain-computer interface experiment. Its aim is to ask the measured participant to pick a number between 1 and 9. Then, he or she is exposed to corresponding visual stimuli and experimenters try to guess the number thought while they are observing event-related potential waveforms on-line. 250 school-age children participated in the experiments that were carried out in elementary and secondary schools in the Czech Republic. Electroencephalographic data from three EEG channels (Fz, Cz, Pz) and stimuli markers were stored. Additional metadata about the participants were collected (gender, age, laterality, the number thought by the participant, the guess of the experimenters, and various interesting additional information). Consequently, we offer the largest publicly available odd-ball paradigm collection of datasets to neuroscientific and brain-computer interface community.

BRŮHA, P., MOUČEK, R., ŠNEJDAR, P., BOHMANN, D., KRAFT, V., ŘEHOŘ, P. Exercise and Wellness Health Strategy Framework Software Prototype for Rapid Collection and Storage of Heterogeneous Health Related Data. In Proceedings of BIOSTEC 2017 - Volume 5: HEALTHINF. Setúbal: SciTePreess, 2017. p. 477-483. ISBN 978-989-758-213-4
Abstract: Unwillingness of many people to assume responsibilities for a personal health, fitness and wellness seems to be widespread. This can be partially remedied by individualized exercise and wellness program that integrates the basic knowledge domains: lifestyle, sports and fitness, and nutrition and personal/environmental health. However, collection, management and analysis of data and metadata related to these domains is demanding and time consuming task. Moreover, the appropriate annotation of raw data is crucial for their next processing. To promote such a program a software infrastructure for collection, storage, management, analysis and interpretation of health related data and metadata has been proposed and part of this infrastructure has been developed and tested outside laboratory conditions. This software prototype allows experimenters to collect various heterogeneous health related data in a highly organized and efficient way. Data are then evaluated and users can view relevant information related to their health and fitness.

MOUČEK, R., HNOJSKÝ, L., VAŘEKA, L., PROKOP, T., BRŮHA, P. Experimental Design and Collection of Brain and Respiratory Data for Detection of Driver´s Attention. In BIOSTEC 2017 Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies Volume 5: HEALTHINF. Setúbal: SciTePress, 2017. p. 441-450. ISBN 978-989-758-213-4
Abstract: Attention of drivers is very important for road safety and it is worth observing even in laboratory conditions during a simulated drive. This paper deals with design of an experiment investigating driver?s attention, validation of collected data, and first preprocessing and processing steps used within data analysis. Brain activity is considered as a primary biosignal and is measured and analyzed using the techniques and methods of electroencephalography and event related potentials. Respiration is considered as a secondary biosignal that is captured together with brain activity. Validation of collected data using a stacked autoencoder is emphasized as an important step preceding data analysis.

VAŘEKA, L., MAUTNER, P. Stacked Autoencoders for the P300 Component Detection. Frontiers in Neuroscience, 2017, Volume 11, Issue 302, p. 1-9. ISSN: 1662-453X
Abstract: Using a combination of unsupervised pre-training and subsequent fine-tuning, deep neural networks have become one of the most reliable classification methods. The aim of the experiments subsequently presented was to verify if deep learning-based models can also perform well for single trial P300 classification with possible application to P300-based brain-computer interfaces. The P300 data used were recorded in the EEG/ERP laboratory at the Department of Computer Science and Engineering, University of West Bohemia, and are publicly available. Stacked autoencoders were implemented and compared with some of the currently most reliable state-of-the-art methods, such as LDA and multi-layer perceptron. The parameters of stacked autoencoders were optimized empirically. Subsequently, fine-tuning using backpropagation was performed. The architecture of the neural network was 209-130-100-50-20-2. The classifiers were trained on a dataset merged from four subjects and subsequently tested on different 11 subjects without further training. The trained SAE achieved 69.2% accuracy that was higher (p < 0.01) than the accuracy of MLP (64.9%) and LDA (65.9%). The recall of 58.8% was slightly higher when compared with MLP (56.2%) and LDA (58.4%).

KROUPA, L., VÁVRA, F., NOVÝ, P. Statistic of Quasi?Periodical Signal with Random Period ? First Application on Vocal Cords Oscillation. In 16th CONFERENCE ON APPLIMAT MATHEMATICS APLIMAT 2017 PROCEEDINGS. Bratislava: Vydavateľstvo Spektrum STU, Bratislava, 2017. p. 905-911. ISBN 978-80-227-4650-2
Abstract: This paper will introduce problem of statistics of quasi-periodic signal in relation to detection of the vocal cords pathology from audio recording. Distribution function of period lengths and its relation to distribution function of immediate frequencies is defined and application on vocal cord diagnostic by classification of periods and frequencies to common (normal) and anomalous is devised.