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Stańczyk J, Pałczyński K, Dzimińska P, Ledziński D, Andrysiak T, Licznar P. The impact of the number of high temporal resolution water meters on the determinism of water consumption in a district metered area. Sci Rep 2023; 13:18921. [PMID: 37919417 PMCID: PMC10622531 DOI: 10.1038/s41598-023-46086-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 10/27/2023] [Indexed: 11/04/2023] Open
Abstract
Developments in data mining techniques have significantly influenced the progress of Intelligent Water Systems (IWSs). Learning about the hydraulic conditions enables the development of increasingly reliable predictive models of water consumption. The non-stationary, non-linear, and inherent stochasticity of water consumption data at the level of a single water meter means that the characteristics of its determinism remain impossible to observe and their burden of randomness creates interpretive difficulties. A deterministic model of water consumption was developed based on data from high temporal resolution water meters. Seven machine learning algorithms were used and compared to build predictive models. In addition, an attempt was made to estimate how many water meters data are needed for the model to bear the hallmarks of determinism. The most accurate model was obtained using Support Vector Regression (8.9%) and the determinism of the model was achieved using time series from eleven water meters of multi-family buildings.
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Affiliation(s)
- Justyna Stańczyk
- Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, Grunwaldzki Sq. 24, 50-363, Wroclaw, Poland.
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Bydgoszcz University of Science and Technology, Computer Science and Electrical Engineering, Profesora Sylwestra Kaliskiego 7 St, 85-796, Bydgoszcz, Poland
| | - Paulina Dzimińska
- MWiK-The Water Supply and Sewerage Company of Bydgoszcz Sp. zo.o., 103 Toruńska St., 85-817, Bydgoszcz, Poland
| | - Damian Ledziński
- Faculty of Telecommunications, Bydgoszcz University of Science and Technology, Computer Science and Electrical Engineering, Profesora Sylwestra Kaliskiego 7 St, 85-796, Bydgoszcz, Poland
| | - Tomasz Andrysiak
- Faculty of Telecommunications, Bydgoszcz University of Science and Technology, Computer Science and Electrical Engineering, Profesora Sylwestra Kaliskiego 7 St, 85-796, Bydgoszcz, Poland
| | - Paweł Licznar
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska St. 20, 00-653, Warszawa, Poland
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Kloska SM, Pałczyński K, Marciniak T, Talaśka T, Wysocki BJ, Davis P, Wysocki TA. Publisher Correction: Integrating glycolysis, citric acid cycle, pentose phosphate pathway, and fatty acid beta-oxidation into a single computational model. Sci Rep 2023; 13:18755. [PMID: 37907577 PMCID: PMC10618546 DOI: 10.1038/s41598-023-45724-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023] Open
Affiliation(s)
- Sylwester M Kloska
- Faculty of Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-094, Bydgoszcz, Poland.
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Beata J Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA
| | - Paul Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA
| | - Tadeusz A Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, 68182, USA
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3
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Kloska A, Giełczyk A, Grzybowski T, Płoski R, Kloska SM, Marciniak T, Pałczyński K, Rogalla-Ładniak U, Malyarchuk BA, Derenko MV, Kovačević-Grujičić N, Stevanović M, Drakulić D, Davidović S, Spólnicka M, Zubańska M, Woźniak M. A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe. Int J Mol Sci 2023; 24:15095. [PMID: 37894775 PMCID: PMC10606184 DOI: 10.3390/ijms242015095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/03/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.
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Affiliation(s)
- Anna Kloska
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
- Faculty of Medical Sciences, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Agata Giełczyk
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Tomasz Grzybowski
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
| | - Rafał Płoski
- Department of Medical Genetics, Warsaw Medical University, 02106 Warsaw, Poland
| | - Sylwester M. Kloska
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
- Faculty of Medical Sciences, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85796 Bydgoszcz, Poland
| | - Urszula Rogalla-Ładniak
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
| | - Boris A. Malyarchuk
- Institute of Biological Problems of the North, Russian Academy of Sciences, 685000 Magadan, Russia
| | - Miroslava V. Derenko
- Institute of Biological Problems of the North, Russian Academy of Sciences, 685000 Magadan, Russia
| | - Nataša Kovačević-Grujičić
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11042 Belgrade, Serbia
| | - Milena Stevanović
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11042 Belgrade, Serbia
- Faculty of Biology, University of Belgrade, 11000 Belgrade, Serbia
- Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia
| | - Danijela Drakulić
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11042 Belgrade, Serbia
| | - Slobodan Davidović
- Institute for Biological Research “Siniša Stanković”, National Institute of Republic of Serbia, University of Belgrade, 11060 Belgrade, Serbia
| | | | - Magdalena Zubańska
- Faculty of Law and Administration, Department of Criminology and Forensic Sciences, University of Warmia and Mazury, 10726 Olsztyn, Poland
| | - Marcin Woźniak
- Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland
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Kloska SM, Pałczyński K, Marciniak T, Talaśka T, Wysocki BJ, Davis P, Wysocki TA. Integrating glycolysis, citric acid cycle, pentose phosphate pathway, and fatty acid beta-oxidation into a single computational model. Sci Rep 2023; 13:14484. [PMID: 37660197 PMCID: PMC10475038 DOI: 10.1038/s41598-023-41765-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 08/31/2023] [Indexed: 09/04/2023] Open
Abstract
The metabolic network of a living cell is highly intricate and involves complex interactions between various pathways. In this study, we propose a computational model that integrates glycolysis, the pentose phosphate pathway (PPP), the fatty acids beta-oxidation, and the tricarboxylic acid cycle (TCA cycle) using queueing theory. The model utilizes literature data on metabolite concentrations and enzyme kinetic constants to calculate the probabilities of individual reactions occurring on a microscopic scale, which can be viewed as the reaction rates on a macroscopic scale. However, it should be noted that the model has some limitations, including not accounting for all the reactions in which the metabolites are involved. Therefore, a genetic algorithm (GA) was used to estimate the impact of these external processes. Despite these limitations, our model achieved high accuracy and stability, providing real-time observation of changes in metabolite concentrations. This type of model can help in better understanding the mechanisms of biochemical reactions in cells, which can ultimately contribute to the prevention and treatment of aging, cancer, metabolic diseases, and neurodegenerative disorders.
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Affiliation(s)
- Sylwester M Kloska
- Faculty of Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-094, Bydgoszcz, Poland.
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Beata J Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA
| | - Paul Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA
| | - Tadeusz A Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, 68182, USA
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Kloska SM, Pałczyński K, Marciniak T, Talaśka T, Miller M, Wysocki BJ, Davis P, Wysocki TA. Conversion of fat to cellular fuel-Fatty acids β-oxidation model. Comput Biol Chem 2023; 104:107860. [PMID: 37028176 DOI: 10.1016/j.compbiolchem.2023.107860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 02/08/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023]
Abstract
β-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a β-oxidation model of fatty acids based on queueing theory. It uses Michaelis-Menten enzyme kinetics, and literature data on metabolites' concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has been validated against experimental data. There are diseases that change the metabolism of fatty acids and the presented model can be used to understand the cause of these changes, analyze metabolites abnormalities, and determine the initial target of treatment.
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Affiliation(s)
- Sylwester M Kloska
- Department of Forensic Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, Bydgoszcz, Poland.
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Marissa Miller
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, USA
| | - Beata J Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, USA
| | - Paul Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, USA
| | - Tadeusz A Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland; Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, USA
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Kloska SM, Pałczyński K, Marciniak T, Talaśka T, Miller M, Wysocki BJ, Davis PH, Soliman GA, Wysocki TA. Queueing theory model of mTOR complexes' impact on Akt-mediated adipocytes response to insulin. PLoS One 2022; 17:e0279573. [PMID: 36574435 PMCID: PMC9794039 DOI: 10.1371/journal.pone.0279573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/11/2022] [Indexed: 12/28/2022] Open
Abstract
A queueing theory based model of mTOR complexes impact on Akt-mediated cell response to insulin is presented in this paper. The model includes several aspects including the effect of insulin on the transport of glucose from the blood into the adipocytes with the participation of GLUT4, and the role of the GAPDH enzyme as a regulator of mTORC1 activity. A genetic algorithm was used to optimize the model parameters. It can be observed that mTORC1 activity is related to the amount of GLUT4 involved in glucose transport. The results show the relationship between the amount of GAPDH in the cell and mTORC1 activity. Moreover, obtained results suggest that mTORC1 inhibitors may be an effective agent in the fight against type 2 diabetes. However, these results are based on theoretical knowledge and appropriate experimental tests should be performed before making firm conclusions.
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Affiliation(s)
- Sylwester M. Kloska
- Department of Forensic Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, Bydgoszcz, Poland
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
| | - Marissa Miller
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, Nebraska, United States of America
| | - Beata J. Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
| | - Paul H. Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
| | - Ghada A. Soliman
- Department of Environmental, Occupational, and Geospatial Health Sciences, City University of New York, Graduate School of Public Health and Healthy Policy, New York, NY, United States of America
| | - Tadeusz A. Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, Nebraska, United States of America
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Pałczyński K, Ledziński D, Andrysiak T. Entropy Measurements for Leukocytes' Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification. Entropy (Basel) 2022; 24:1560. [PMID: 36359651 PMCID: PMC9689677 DOI: 10.3390/e24111560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
The study of leukemia classification using deep learning techniques has been conducted by multiple research teams worldwide. Although deep convolutional neural networks achieved high quality of sick vs. healthy patient discrimination, their inherent lack of human interpretability of the decision-making process hinders the adoption of deep learning techniques in medicine. Research involving deep learning proved that distinguishing between healthy and sick patients using microscopic images of lymphocytes is possible. However, it could not provide information on the intermediate steps in the diagnosis process. As a result, despite numerous examinations, it is still unclear whether the lymphocyte is the only object in the microscopic picture containing leukemia-related information or if the leukocyte's surroundings also contain the desired information. In this work, entropy measures and machine learning models were applied to study the informativeness of both whole images and lymphocytes' surroundings alone for Leukemia classification. This work aims to provide human-interpretable features marking the probability of sickness occurrence. The research stated that the hue distribution of images with lymphocytes obfuscated alone is informative enough to facilitate 93.0% accuracy in healthy vs. sick classification. The research was conducted on the ALL-IDB2 dataset.
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Kloska SM, Pałczyński K, Marciniak T, Talaśka T, Miller M, Wysocki BJ, Davis P, Wysocki TA. Queueing theory model of pentose phosphate pathway. Sci Rep 2022; 12:4601. [PMID: 35301361 PMCID: PMC8930976 DOI: 10.1038/s41598-022-08463-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/08/2022] [Indexed: 11/25/2022] Open
Abstract
Due to its role in maintaining the proper functioning of the cell, the pentose phosphate pathway (PPP) is one of the most important metabolic pathways. It is responsible for regulating the concentration of simple sugars and provides precursors for the synthesis of amino acids and nucleotides. In addition, it plays a critical role in maintaining an adequate level of NADPH, which is necessary for the cell to fight oxidative stress. These reasons prompted the authors to develop a computational model, based on queueing theory, capable of simulating changes in PPP metabolites’ concentrations. The model has been validated with empirical data from tumor cells. The obtained results prove the stability and accuracy of the model. By applying queueing theory, this model can be further expanded to include successive metabolic pathways. The use of the model may accelerate research on new drugs, reduce drug costs, and reduce the reliance on laboratory animals necessary for this type of research on which new methods are tested.
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Affiliation(s)
- Sylwester M Kloska
- Faculty of Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-094, Bydgoszcz, Poland.
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland
| | - Marissa Miller
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, 68182, USA
| | - Beata J Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA
| | - Paul Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA
| | - Tadeusz A Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796, Bydgoszcz, Poland. .,Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, 68182, USA.
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Śmigiel S, Pałczyński K, Ledziński D. Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset. Sensors (Basel) 2021; 21:8174. [PMID: 34960267 PMCID: PMC8705269 DOI: 10.3390/s21248174] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/21/2021] [Accepted: 12/03/2021] [Indexed: 11/16/2022]
Abstract
Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.
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Affiliation(s)
- Sandra Śmigiel
- Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland; (K.P.); (D.L.)
| | - Damian Ledziński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland; (K.P.); (D.L.)
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10
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Kloska S, Pałczyński K, Marciniak T, Talaśka T, Nitz M, Wysocki BJ, Davis P, Wysocki TA. Queueing theory model of Krebs cycle. Bioinformatics 2021; 37:2912-2919. [PMID: 33724355 DOI: 10.1093/bioinformatics/btab177] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/08/2021] [Accepted: 03/11/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Queueing theory can be effective in simulating biochemical reactions taking place in living cells, and the article paves a step toward development of a comprehensive model of cell metabolism. Such a model could help to accelerate and reduce costs for developing and testing investigational drugs reducing number of laboratory animals needed to evaluate drugs. RESULTS The article presents a Krebs cycle model based on queueing theory. The model allows for tracking of metabolites concentration changes in real time. To validate the model, a drug-induced inhibition affecting activity of enzymes involved in Krebs cycle was simulated and compared with available experimental data. AVAILABILITYAND IMPLEMENTATION The source code is freely available for download at https://github.com/UTP-WTIiE/KrebsCycleUsingQueueingTheory, implemented in C# supported in Linux or MS Windows. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sylwester Kloska
- Faculty of Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-067 Bydgoszcz, Poland
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Marissa Nitz
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA
| | - Beata J Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Paul Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Tadeusz A Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland.,Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA
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11
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Śmigiel S, Pałczyński K, Ledziński D. ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset. Entropy (Basel) 2021; 23:e23091121. [PMID: 34573746 PMCID: PMC8469424 DOI: 10.3390/e23091121] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/18/2021] [Accepted: 08/25/2021] [Indexed: 01/14/2023]
Abstract
The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.
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Affiliation(s)
- Sandra Śmigiel
- Faculty of Mechanical Engineering, UTP University of Science and Technology in Bydgoszcz, 85-796 Bydgoszcz, Poland
- Correspondence: ; Tel.: +48-52-340-8346
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology in Bydgoszcz, 85-796 Bydgoszcz, Poland; (K.P.); (D.L.)
| | - Damian Ledziński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology in Bydgoszcz, 85-796 Bydgoszcz, Poland; (K.P.); (D.L.)
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