Institute of Medical Technology and Equipment ITAM

Zabrze, Poland

Institute of Medical Technology and Equipment ITAM

Zabrze, Poland
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Henzel N.,Institute of Medical Technology and Equipment ITAM
Advances in Intelligent Systems and Computing | Year: 2017

The principal objective of this project was to investigate the detection of QRS complexes in noisy ECG signals. This study provides a novel approach to the construction of a QRS detector based on the Ensemble Empirical Mode Decomposition. The detection function is based on predicted probability that the current signal sample is a QRS fiducial point. The performances of the proposed method were verified on the MIT-BIH Arrhythmia Database. Results showed that this approach improves the QRS detection accuracy. © Springer International Publishing AG 2017.


Kotas M.,Silesian University of Technology | Jezewski J.,Institute of Medical Technology and Equipment ITAM | Horoba K.,Institute of Medical Technology and Equipment ITAM | Matonia A.,Institute of Medical Technology and Equipment ITAM
Computer Methods and Programs in Biomedicine | Year: 2011

In this paper we propose a new structure of the instrumentation for electrocardiographic fetal monitoring. We apply a single-channel approach to maternal electrocardiogram suppression in the recorded four abdominal bioelectric signals. Then we exploit spatial and temporal properties of the extracted four-channel fetal electrocardiogram to construct a new channel with higher signal-to-noise ratio. Finally, we perform detection of fetal QRS complexes. The proposed approach is investigated with the help of the constructed database of the maternal abdominal signals. During the detection tests, the spatio-temporal filtering allowed us to decrease significantly the number of the detection errors of different detectors applied. Moreover, we present visually that even if the fetal QRS complexes are buried in noise, the spatio-temporal filtering can produce the signal with the discernible ones. © 2010 Elsevier Ireland Ltd.


Pedrycz W.,Polish Academy of Sciences | Gacek A.,Institute of Medical Technology and Equipment ITAM | Wang X.,Hubei University
Pattern Recognition Letters | Year: 2015

In this study, a paradigm of fuzzy clustering is augmented by available domain knowledge expressed in the form of relational constraints built with the aid of a collection of fuzzy sets. These constraints are described as a collection of Cartesian products of fuzzy sets or their logic expressions are used to form an augmented data space and transform nonlinearly original data. Depending upon the nature of the constraints, discussed are two categories of resulting representations (clustering spaces), namely homogeneous spaces (in case when the transformations are fully expressed by means of the constraints) and heterogeneous spaces (when the resulting space is composed of some original variables present in the initially available data space and those being transformed and expressed by means of satisfaction levels of the constraints). The role of information granules of order-2 is revealed with regard to results of clustering produced in the transformed space. A generalization of the proposed approach is also discussed in case the clustered data are not numeric but are provided in the form of information granules; in this case a special attention is paid to a way in which a representation (description) of information granules is realized through relational constraints. We elaborate on the formation of the space (induced by constraints) and original data as well as discuss the detailed algorithmic developments. © 2015 Elsevier B.V. All rights reserved.


Gacek A.,Institute of Medical Technology and Equipment ITAM | Pedrycz W.,University of Alberta | Pedrycz W.,King Abdulaziz University
Soft Computing | Year: 2013

This study provides a general introduction to the principles, algorithms and practice of computational intelligence (CI) and elaborates on those facets with relation to biomedical signal analysis, especially ECG signals. We discuss the main technologies of computational intelligence (namely, neural networks, fuzzy sets or granular computing, and evolutionary optimization), identify their focal points and stress an overall synergistic character, which ultimately gives rise to the highly symbiotic CI environment. Furthermore, the main advantages and limitations of the CI technologies are discussed. In the sequel, we present CI-oriented constructs in signal modeling, classification, and interpretation. Examples of the CI-based ECG signal processing problems are presented. © 2013 Springer-Verlag Berlin Heidelberg.


Gacek A.,Institute of Medical Technology and Equipment ITAM
Artificial Intelligence in Medicine | Year: 2013

Objective: The study introduces and elaborates on a certain perspective of biomedical data analysis where data structure is revealed through fuzzy clustering. The key objective of the study is to develop a characterization of the content of the clusters by offering a number of their descriptors established on the basis of membership grades of patterns included there, as well as on the basis of their class membership. Next, a design of a cluster-based classifier is presented in which the structure of the classifier is based on a collection of clusters. The structure also exploits the descriptors of the clusters as well as aggregates their characteristics with the activation levels of the associated clusters formed in the feature space in which QRS complexes are represented. Methods and materials: The underlying methods involve the use of fuzzy clustering and two essential ways of representing QRS complexes with the use of the Hermite expansion of signals and piecewise aggregate approximation (PAA). The material involves QRS segments coming from the MIT-BIH Arrhythmia Database. Results: The key results demonstrate and quantify the effectiveness of QRS characterization with the use of clustering realized in the space of coefficients of the Hermite series expansion and the PAA expansion. In general, accuracy of the discussed classification schemes increases with the increase of the number of clusters; the difference varies in the range of 30% (when moving from 10 to 60 clusters). The fuzzification coefficient of the fuzzy C-Means clustering algorithm has a visible impact on the quality of the results in the range of up 40% difference in the classification of accuracy (when the coefficient varies in-between 1.1 and 2.5). The PAA representation space leads to slightly better results than those obtained when using the Hermite representation of the signals, the difference is of around 5%. Conclusions: It was shown that granular representation of electrocardiographic signals is essential to data analysis and classification by providing a means to reveal and characterize the data structure and by providing prerequisites to construct pattern classifiers. The study also shows that fuzzy clusters deliver important structural information about the data that could be further quantified by looking into the content of clusters. © 2013 Elsevier B.V.


Gacek A.,Institute of Medical Technology and Equipment ITAM
Information Sciences | Year: 2013

In spite of the evident diversity of models of signals and time series, there is still an urgent need to develop constructs that are both accurate and highly interpretable (human-centric). While a great deal of research has been devoted to the design of nonlinear models of time series (with anticipation of achieving high accuracy of prediction), an issue of interpretability (transparency) of the models remains an evident and ongoing challenge. The user-friendliness of models of time series comes hand in hand with an ability of humans to perceive and process abstract entities rather than plain numeric entities. With this regard, information granules and Granular Computing play an essential role. The use of information granules gives rise to a concept of granular models of time series or granular models of signals and time series, in brief. A granular interpretation of temporal data, where the role of information granularity is of paramount interest and effectively supports a human-centric description of relationships existing within data. This study revisits generic concepts of information granules and Granular Computing in this setting and elaborates on a fundamental way of forming information granules (both sets - intervals as well as fuzzy sets) through applying a principle of justifiable granularity. The granular representation of time series is then discussed with a number of representation alternatives. A question of forming adjustable temporal slices (time windows) using which information granules are formed is discussed. With this regard presented is an optimization criterion of a sum of volumes of information granules whose minimization is realized through some methods of evolutionary or population-based optimization techniques. A series of illustrative examples is also discussed. © 2012 Elsevier Inc. All rights reserved.


Henzel N.,Institute of Medical Technology and Equipment ITAM | Wrbel J.,Institute of Medical Technology and Equipment ITAM | Horoba K.,Institute of Medical Technology and Equipment ITAM
Proceedings of the 23rd International Conference Mixed Design of Integrated Circuits and Systems, MIXDES 2016 | Year: 2016

QRS detection plays a key role in processing, measurements and analysis of ECG signals. There is a recognized need for reliable and objective evaluation of their performances. This task becomes difficult taking into account multiple data source, different data storage formats as well as various execution environments and modes. The aim of this project was to develop an automatic, configurable and adaptable to various conditions system for quantitative evaluation of QRS detectors. This paper presents the general structure of this system. © 2016 Department of Microelectronics and Computer Science, Lodz University of Technology.


Gacek A.,Institute of Medical Technology and Equipment ITAM
Applied Soft Computing Journal | Year: 2015

This study provides a general introduction to the principles, algorithms and practice of Computational Intelligence (CI) and elaborates on their use to signal processing and time series. In this setting, we discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets or Granular Computing, and evolutionary optimization), identify their focal points and stress an overall synergistic character, which ultimately gives rise to the highly synergistic CI environment. Furthermore, the main advantages and limitations of the CI technologies are discussed. In the sequel, we present CI-oriented constructs in signal modeling, classification, and interpretation. © 2014 Elsevier B.V. All rights reserved.


Gacek A.,Institute of Medical Technology and Equipment ITAM
Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013 | Year: 2013

Fuzzy clustering has been one of the commonly used vehicles to construct information granules (whose description is provided in terms of prototypes and partition matrices). The quality of resulting information granules can be assessed by quantifying how well the original numeric data from which information granules have been constructed can be represented (granulated) by information granules and subsequently reconstructed (degranulated). We recall the concept of the reconstruction process and show how the inevitable reconstruction errors can be handled (and reduced) by granular generalizations of the representatives of fuzzy clusters, namely granular propototypes (hyperboxes) and granular (interval-valued) partition matrices. Their construction is presented in detail, several ways of reconstruction are discussed and an optimization problem of the level of information granularity involved is raised. © 2013 IEEE.


Gacek A.,Institute of Medical Technology and Equipment ITAM
Expert Systems with Applications | Year: 2011

The objective of this paper is to consider self-organizing maps (SOMs) as a vehicle for analysis of ECG data and making decisions as to further preprocessing and selecting classification algorithms. In contrast to other commonly used methods of unsupervised learning (such as e.g., Fuzzy C-Means or K-Means), the results formed by SOMs are more user-oriented allowing for an intensive interaction with the user in supporting various tasks of "what-if" analysis. In this manner, the map scan serve as a preliminary vehicle supporting a detailed system design. In the study, the standard model of SOM is augmented by several interpretation-oriented features such as region analysis and feature descriptors. The map helps reveal a structure in a set of ECG patterns and visualize a topology of such data. The role of the designer of any subsequent classifier or signal analyzer is associated with an inspection of some already visualized regions of the self-organizing map characterized by a significant level of data homogeneity and based on the discovered topology, make decisions as to the development of classification schemes. The experimental part illustrating the proposed design practices is concerned with the data coming from the MIT-BIH ECG database being commonly utilized in the realm of ECG signal analysis and classifier design. © 2011 Elsevier Ltd. All rights reserved.

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