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Information Theory in Biomedical Data Mining

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 14816

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Prof. Dr. Małgorzata Syczewska

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Guest Editor

Department of Rehabilitation, Children's Memorial Health Institute, 04-730 Warszawa, Poland
Interests: gait analysis; balance; motor control; biomechanics; modelling; human motion

Special Issue Information

Dear Colleagues,

The rapid development of medicine and biomedical sciences provides us with an increasing volume of collected data, which are difficult to analyze and model. In standard models, the researchers define the segments of data and the connections they analyze, based on their experience, physiology, or previous studies, but such an approach, although widely used, bears the risk of losing vital pieces of information, and in some cases, the conclusions are definitely less meaningful than they could be.

Methods of data mining can be applied to big data sets of collected biomedical data bases, such as machine learning, various classification trees algorithms, genetic algorithms, nonlinear relationships in complex models, etc. However, information theory methods are also gradually more widely used to establish causal connections in big data sets and to get more insight into the nature of the phenomena described by the biomedical data.

The growing interest in such studies is what has inspired this Special Issue of Entropy. The aim of this Special Issue is to provide an opportunity to exchange ideas on using a new approach towards biomedical data. We would like to invite researchers dealing with application of entropy to various sets of biomedical data, in the hope that entropy can become a key to the complex biomedical systems, which will help to establish meaningful relationships, patterns, and, generally, better understanding of physiological processes.

Prof. Małgorzata Syczewska
Guest Editor

Manuscript Submission Information

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Keywords

biomedical data
big data
entropy for data mining
nonlinear relationships
information theory

Published Papers (6 papers)

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Research

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17 pages, 4797 KiB

Open AccessArticle

Multiscale Permutation Lempel–Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals

by Marta Borowska

Entropy 2021, 23(7), 832; https://doi.org/10.3390/e23070832 - 29 Jun 2021

Cited by 20 | Viewed by 2272

Abstract

This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%. Full article

(This article belongs to the Special Issue Information Theory in Biomedical Data Mining)

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15 pages, 1167 KiB

Open AccessArticle

Knowledge Discovery from Medical Data and Development of an Expert System in Immunology

by Małgorzata Pac, Irina Mikutskaya and Jan Mulawka

Entropy 2021, 23(6), 695; https://doi.org/10.3390/e23060695 - 31 May 2021

Cited by 3 | Viewed by 2113

Abstract

Artificial intelligence is one of the fastest-developing areas of science that covers a remarkably wide range of problems to be solved. It has found practical application in many areas of human activity, also in medicine. One of the directions of cooperation between computer science and medicine is to assist in diagnosing and proposing treatment methods with the use of IT tools. This study is the result of collaboration with the Children’s Memorial Health Institute in Warsaw, from where a database containing information about patients suffering from Bruton’s disease was made available. This is a rare disorder, difficult to detect in the first months of life. It is estimated that one in 70,000 to 90,000 children will develop Bruton’s disease. But even these few cases need detailed attention from doctors. Based on the data contained in the database, data mining was performed. During this process, knowledge was discovered that was presented in a way that is understandable to the user, in the form of decision trees. The best models obtained were used for the implementation of expert systems. Based on the data introduced by the user, the system conducts expertise and determines the severity of the course of the disease or the severity of the mutation. The CLIPS language was used for developing the expert system. Then, using this language, software was developed producing six expert systems. In the next step, experimental verification was performed, which confirmed the correctness of the developed systems. Full article

(This article belongs to the Special Issue Information Theory in Biomedical Data Mining)

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13 pages, 798 KiB

Open AccessArticle

The Impact of Visual Input and Support Area Manipulation on Postural Control in Subjects after Osteoporotic Vertebral Fracture

by Michalina Błażkiewicz, Justyna Kędziorek and Anna Hadamus

Entropy 2021, 23(3), 375; https://doi.org/10.3390/e23030375 - 20 Mar 2021

Cited by 6 | Viewed by 2258

Abstract

Osteoporosis is a prevalent health concern among older adults and is associated with an increased risk of falls that may result in fracture, injury, or even death. Identifying the risk factors for falls and assessing the complexity of postural control within this population is essential for developing effective regimes for fall prevention. The aim of this study was to assess postural control in individuals recovering from osteoporotic vertebral fractures while performing various stability tasks. Seventeen individuals with type II osteoporosis and 17 healthy subjects participated in this study. The study involved maintaining balance while standing barefoot on both feet for 20 s on an Advanced Mechanical Technology Inc. (AMTI) plate, with eyes open, eyes closed, and eyes closed in conjunction with a dual-task. Another three trials lasting 10 s each were undertaken during a single-leg stance under the same conditions. Fall risk was assessed using the Biodex Balance platform. Nonlinear measures were used to assess center of pressure (CoP) dynamics in all trials. Reducing the support area or elimination of the visual control led to increased sample entropy and fractal dimension. Results of the nonlinear measurements indicate that individuals recovering from osteoporotic vertebral fractures are characterized by decreased irregularity, mainly in the medio-lateral direction and reduced complexity. Full article

(This article belongs to the Special Issue Information Theory in Biomedical Data Mining)

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11 pages, 1608 KiB

Open AccessArticle

Are Gait and Balance Problems in Neurological Patients Interdependent? Enhanced Analysis Using Gait Indices, Cyclograms, Balance Parameters and Entropy

by Malgorzata Syczewska, Ewa Szczerbik, Malgorzata Kalinowska, Anna Swiecicka and Grazyna Graff

Entropy 2021, 23(3), 359; https://doi.org/10.3390/e23030359 - 17 Mar 2021

Cited by 3 | Viewed by 1755

Abstract

Background: Balance and locomotion are two main complex functions, which require intact and efficient neuromuscular and sensory systems, and their proper integration. In many studies the assumption of their dependence is present, and some rehabilitation approaches are based on it. Other papers undermine this assumption. Therefore the aim of this study was to examine the possible dependence between gait and balance in patients with neurological or sensory integration problems, which affected their balance. Methods: 75 patients (52 with neurological diseases, 23 with sensory integration problems) participated in the study. They underwent balance assessment on Kistler force plate in two conditions, six tests on a Balance Biodex System and instrumented gait analysis with VICON. The gait and balances parameters and indices, together with entropy and cyclograms were used for the analysis. Spearman correlation, multiple regression, cluster analysis, and discriminant analysis were used as analytical tools. Results: The analysis divided patients into 2 groups with 100% correctly classified cases. Some balance and gait measures are better in the first group, but some others in the second. Conclusions: This finding confirms the hypothesis that there is no direct link between gait and balance deficits. Full article

(This article belongs to the Special Issue Information Theory in Biomedical Data Mining)

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9 pages, 679 KiB

Open AccessArticle

Assessment of the Effectiveness of Rehabilitation after Total Knee Replacement Surgery Using Sample Entropy and Classical Measures of Body Balance

by Anna Hadamus, Dariusz Białoszewski, Michalina Błażkiewicz, Aleksandra J. Kowalska, Edyta Urbaniak, Kamil T. Wydra, Karolina Wiaderna, Rafał Boratyński, Agnieszka Kobza and Wojciech Marczyński

Entropy 2021, 23(2), 164; https://doi.org/10.3390/e23020164 - 29 Jan 2021

Cited by 12 | Viewed by 2582

Abstract

Exercises in virtual reality (VR) have recently become a popular form of rehabilitation and are reported to be more effective than a standard rehabilitation protocol alone. The aim of this study was to assess the efficacy of adjunct VR training in improving postural control in patients after total knee replacement surgery (TKR). Forty-two patients within 7–14 days of TKR were enrolled and divided into a VR group and a control group (C). The C group underwent standard postoperative rehabilitation. The VR group additionally attended twelve 30-min exercise sessions using the Virtual Balance Clinic prototype system. Balance was assessed on the AMTI plate in bipedal standing with and without visual feedback before and after the four-week rehabilitation. Linear measures and sample entropy of CoP data were analyzed. After four weeks of rehabilitation, a significant reduction in parameters in the sagittal plane and ellipse area was noted while the eyes remained open. Regression analysis showed that sample entropy depended on sex, body weight, visual feedback and age. Based on the sample entropy results, it was concluded that the complexity of the body reaction had not improved. The standing-with-eyes-closed test activates automatic balance mechanisms and offers better possibilities as a diagnostic tool. Full article

(This article belongs to the Special Issue Information Theory in Biomedical Data Mining)

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Review

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24 pages, 506 KiB

Open AccessReview

Nonlinear Measures to Evaluate Upright Postural Stability: A Systematic Review

by Justyna Kędziorek and Michalina Błażkiewicz

Entropy 2020, 22(12), 1357; https://doi.org/10.3390/e22121357 - 30 Nov 2020

Cited by 51 | Viewed by 3177

Abstract

Conventional biomechanical analyses of human movement have been generally derived from linear mathematics. While these methods can be useful in many situations, they fail to describe the behavior of the human body systems that are predominately nonlinear. For this reason, nonlinear analyses have become more prevalent in recent literature. These analytical techniques are typically investigated using concepts related to variability, stability, complexity, and adaptability. This review aims to investigate the application of nonlinear metrics to assess postural stability. A systematic review was conducted of papers published from 2009 to 2019. Databases searched were PubMed, Google Scholar, Science-Direct and EBSCO. The main inclusion consisted of: Sample entropy, fractal dimension, Lyapunov exponent used as nonlinear measures, and assessment of the variability of the center of pressure during standing using force plate. Following screening, 43 articles out of the initial 1100 were reviewed including 33 articles on sample entropy, 10 articles on fractal dimension, and 4 papers on the Lyapunov exponent. This systematic study shows the reductions in postural regularity related to aging and the disease or injures in the adaptive capabilities of the movement system and how the predictability changes with different task constraints. Full article

(This article belongs to the Special Issue Information Theory in Biomedical Data Mining)

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