Miroslav Bursa

Open access intrapartum CTG database

BackgroundCardiotocography (CTG) is a monitoring of fetal heart rate and uterine contractions. Since 1960 it is routinely used by obstetricians to assess fetal well-being. Many attempts to introduce methods of automatic signal processing and evaluation have appeared during the last 20 years, however still no significant progress similar to that in the domain of adult heart rate variability, where open access databases are available (e.g. MIT-BIH), is visible. Based on a thorough review of the relevant publications, presented in this paper, the shortcomings of the current state are obvious. A lack of common ground for clinicians and technicians in the field hinders clinically usable progress. Our open access database of digital intrapartum cardiotocographic recordings aims to change that.DescriptionThe intrapartum CTG database consists in total of 552 intrapartum recordings, which were acquired between April 2010 and August 2012 at the obstetrics ward of the University Hospital in Brno, Czech Republic. All recordings were stored in electronic form in the OB TraceVue®;system. The recordings were selected from 9164 intrapartum recordings with clinical as well as technical considerations in mind. All recordings are at most 90 minutes long and start a maximum of 90 minutes before delivery. The time relation of CTG to delivery is known as well as the length of the second stage of labor which does not exceed 30 minutes. The majority of recordings (all but 46 cesarean sections) is – on purpose – from vaginal deliveries. All recordings have available biochemical markers as well as some more general clinical features. Full description of the database and reasoning behind selection of the parameters is presented in the paper.ConclusionA new open-access CTG database is introduced which should give the research community common ground for comparison of results on reasonably large database. We anticipate that after reading the paper, the reader will understand the context of the field from clinical and technical perspectives which will enable him/her to use the database and also understand its limitations.

Information Technology in Bio- and Medical Informatics

The advancement of next generation sequencing (NGS) and shotgun sequencing technologies produced massive amounts of genomics data. Metagenomics, a powerful technique to study genetic material of uncultivable microorganisms received directly from their natural environment, is dealing with high throughput sequencing read data sets. Assembling, binning and alignment of short reads in order to identify microorganisms of a Metagenomics sample are expensive and timeconsuming, regardless of other restrictions. DNA signature is a short nucleotide sequence fragment which is used to distinguish species across all other species. It can be a basis for identifying microorganisms both in environmental and clinical samples directly from the short reads, without assembling and alignment processes. In this paper, we propose a scalable method in which we use optimization techniques borrowed from database technology, namely bitmap indexes. They are used to speed up searching and matching of billions of DNA signatures in the short reads of thousands of different microorganisms, using commodity High Performance Computing, such as Hadoop MapReduce, Hive and Hbase.

Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation

In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing technique. The method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than each method alone. As the method is similar to random forest generation, it can be also used for feature selection. The paper also discusses the parameter estimation for the method. Tests on real data (UCI and real biomedical data) have been performed and evaluated. The average accuracy of the method over MIT-BIH database with normalized data and equalized classes is sensitivity 93.22 % and specificity 87.13 %.

Assessment of features for automatic CTG analysis based on expert annotation

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960s used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchis fractal dimension are among the top five features.

Ant Colony Cooperative Strategy in Electrocardiogram and Electroencephalogram Data Clustering

Cooperation in natural processes is very important feature, which is modeled by many nature-inspired algorithms. Nature inspired metaheuristics have interesting stochastic properties which make them suitable for use in data mining, data clustering and other computationally demanding application areas. It is because they often produce robust solutions in fairly reasonable time. This paper presents an application of clustering method inspired by the behavior of real ants in the nature in biomedical signal processing. The ants cooperatively maintain and evolve a pheromone matrix which is used to select features. The main aim of this study was to design and develop a combination of feature extraction and classification methods for automatic recognition of significant structure in biological signal recordings. The method is targeted towards speeding up and increasing objectivity of identification of important classes and may be used for online classification. Inherent properties of the method make it suitable for analysis of newly incoming data. The method can be also used in the expert classification process. We have obtained significant results in electrocardiogram and electroencephalogram recordings, which justify the use of such method.

Information retrieval from hospital information system

This paper details the process of mining information from a hospital information system that has been designed approximately 15 years ago. The information is distributed within database tables in large textual attributes with a free structure. Information retrieval from these information is necessary for complementing cardiotocography signals with additional information that is to be implemented in a decision support system.The basic statistical overview (n-gram analysis) helped with the insight into data structure, however more sophisticated methods have to be used as human (and expert) processing of the whole data were out of consideration: over 620,000 text fields contained text reports in natural language with (many) typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations.There was a strong need to efficiently determine the overall structure of the database and discover information that is important from the clinical point of view. We have used three different methods: k-means, self-organizing map and a self-organizing approach inspired by ant-colonies that performed clustering of the records. The records were visualized and revealed the most prominent information structure(s) that were consulted with medical experts and served for further mining from the database.The outcome of this task is a set of ordered or nominal attributes with a structural information that is available for rule discovery mining and automated processing for the research of asphyxia prediction during delivery. The proposed methodology has significantly reduced the processing time of loosely structured textual records for both IT and medical experts.

Modified Ant Colony Clustering Method in Long-term Electrocardiogram Processing

The paper presents an application of a clustering technique inspired by ant colony metaheuristics. The paper addresses the problem of long-term (Holter) electrocardiogram data processing. Long-term recording produces a huge amount of biomedical data, which must be preprocessed prior to its presentation to the specialist. The paper also discusses relevant aspects improving the robustness, stability and convergence criteria of the method. The method is compared with well known clustering techniques (both classical and nature-inspired), first testing on the known dataset and finally applying them to the real ECG data records from the MIT-BIH database and outperforms the standard methods. Electrocardiogram data clustering can effectively reduce the amount of data presented to the cardiologist: cardiac arrhythmia and significant morphology changes in the ECG can be visually emphasized in a reasonable time. The final evaluation of the ECG recording must still be made by an expert.

Standardization and Interoperability: Basic Conditions for Efficient Solutions

Information and communication technologies have already become inseparable part of healthcare sector activities. In the paper we discuss the issues of standardization and interoperability that are crucial for correct interconnection of medical and other devices and information systems. Our previous work in the area has led us to the conclusion that successful integration of partial solutions will be strongly dependent on the issue of interoperability of medical devices and information systems. It comprises problems of standardization of data acquisition, communication, processing, and storage; and connected problem: correct data mapping between different ICT applications. We present several examples of partial solutions of communication and data format definition in dedicated areas.

Pelvic floor dysfunction after vaginal and cesarean delivery among singleton primiparas

To compare the prevalence of pelvic floor dysfunction symptoms, including pelvic organ prolapse (POP), urinary incontinence (UI), and fecal incontinence (FI) among primiparous women after vaginal and cesarean delivery.

Practical Problems and Solutions in Hospital Information System Data Mining

Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. Doctors often use natural language in medical records. Therefore it contains many ambiguities due to non-standard abbreviations and synonyms. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medical terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities, syntax errors and many nonstandard abbreviations.