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Candia-Rivera D. Modeling brain-heart interactions from Poincaré plot-derived measures of sympathetic-vagal activity. MethodsX 2023; 10:102116. [PMID: 36970022 PMCID: PMC10034502 DOI: 10.1016/j.mex.2023.102116] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
Recent studies suggest that the interaction between the brain and heart plays a key role in cognitive processes, and measuring these interactions is crucial for understanding the interaction between the central and autonomic nervous systems. However, studying this bidirectional interplay presents methodological challenges, and there is still much room for exploration. This paper presents a new computational method called the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG) for estimating brain-heart interactions. The PSV-SDG combines EEG and cardiac sympathetic-vagal dynamics to provide time-varying and bidirectional estimators of mutual interplay. The method is grounded in the Poincaré plot, a heart rate variability method to estimate sympathetic-vagal activity that can account for potential non-linearities. This algorithm offers a new approach and computational tool for functional assessment of the interplay between EEG and cardiac sympathetic-vagal activity. The method is implemented in MATLAB under an open-source license. • A new brain-heart interaction modeling approach is proposed. • The modeling is based on coupled synthetic data generators of EEG and heart rate series. • Sympathetic and vagal activities are gathered from Poincaré plot geometry.
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Neuro-fuzzy estimation of important coccoliths on abundances of foraminiferal species and fragments abundances. J Radioanal Nucl Chem 2021. [DOI: 10.1007/s10967-021-08044-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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3
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Chen S, Chen L, Zhang X, Yang Z. Screening of cardiac disease based on integrated modeling of heart rate variability. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Rahman S, Habel M, Contrada RJ. Poincaré plot indices as measures of sympathetic cardiac regulation: Responses to psychological stress and associations with pre-ejection period. Int J Psychophysiol 2018; 133:79-90. [DOI: 10.1016/j.ijpsycho.2018.08.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 06/19/2018] [Accepted: 08/09/2018] [Indexed: 12/29/2022]
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Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:7525789. [PMID: 29849551 PMCID: PMC5932517 DOI: 10.1155/2018/7525789] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 01/11/2018] [Accepted: 01/31/2018] [Indexed: 11/17/2022]
Abstract
This paper measures the adhesion/cohesion force among asphalt molecules at nanoscale level using an Atomic Force Microscopy (AFM) and models the moisture damage by applying state-of-the-art Computational Intelligence (CI) techniques (e.g., artificial neural network (ANN), support vector regression (SVR), and an Adaptive Neuro Fuzzy Inference System (ANFIS)). Various combinations of lime and chemicals as well as dry and wet environments are used to produce different asphalt samples. The parameters that were varied to generate different asphalt samples and measure the corresponding adhesion/cohesion forces are percentage of antistripping agents (e.g., Lime and Unichem), AFM tips K values, and AFM tip types. The CI methods are trained to model the adhesion/cohesion forces given the variation in values of the above parameters. To achieve enhanced performance, the statistical methods such as average, weighted average, and regression of the outputs generated by the CI techniques are used. The experimental results show that, of the three individual CI methods, ANN can model moisture damage to lime- and chemically modified asphalt better than the other two CI techniques for both wet and dry conditions. Moreover, the ensemble of CI along with statistical measurement provides better accuracy than any of the individual CI techniques.
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Adaptive neuro-fuzzy inference system for evaluating dysarthric automatic speech recognition (ASR) systems: a case study on MVML-based ASR. Soft comput 2018. [DOI: 10.1007/s00500-018-3013-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Mladenović I, Milovančević M, Sokolov Mladenović S, Marjanović V, Petković B. Analyzing and management of health care expenditure and gross domestic product (GDP) growth rate by adaptive neuro-fuzzy technique. COMPUTERS IN HUMAN BEHAVIOR 2016. [DOI: 10.1016/j.chb.2016.07.052] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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8
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Afifi F, Anuar NB, Shamshirband S, Choo KKR. DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. PLoS One 2016; 11:e0162627. [PMID: 27611312 PMCID: PMC5017788 DOI: 10.1371/journal.pone.0162627] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Accepted: 08/25/2016] [Indexed: 11/19/2022] Open
Abstract
To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).
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Affiliation(s)
- Firdaus Afifi
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Nor Badrul Anuar
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
- * E-mail: (NBA); (KKRC)
| | - Shahaboddin Shamshirband
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Kim-Kwang Raymond Choo
- Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, Texas. United States of America
- School of Information Technology & Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia
- School of Computer Science, China University of Geosciences, Wuhan, China
- * E-mail: (NBA); (KKRC)
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Kolus A, Imbeau D, Dubé PA, Dubeau D. Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system. APPLIED ERGONOMICS 2016; 54:158-168. [PMID: 26851475 DOI: 10.1016/j.apergo.2015.12.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 12/11/2015] [Accepted: 12/15/2015] [Indexed: 06/05/2023]
Abstract
In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment.
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Affiliation(s)
- Ahmet Kolus
- Department of Systems Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.
| | - Daniel Imbeau
- Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada
| | - Philippe-Antoine Dubé
- Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada
| | - Denise Dubeau
- Direction de la recherche forestière, Ministère des Forêts, de la Faune et des Parcs, Québec, Canada
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10
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Recurrence plot of heart rate variability signal in patients with vasovagal syncopes. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.10.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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11
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Lopez-Franco M, Sanchez EN, Alanis AY, Lopez-Franco C, Arana-Daniel N. Decentralized control for stabilization of nonlinear multi-agent systems using neural inverse optimal control. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.06.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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A combination of computational fluid dynamics (CFD) and adaptive neuro-fuzzy system (ANFIS) for prediction of the bubble column hydrodynamics. POWDER TECHNOL 2015. [DOI: 10.1016/j.powtec.2015.01.038] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Pourtousi M, Zeinali M, Ganesan P, Sahu JN. Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS. RSC Adv 2015. [DOI: 10.1039/c5ra11583c] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
This work presents a combination of Computational Fluid Dynamics (CFD) and Adaptive Network-based Fuzzy Inference System (ANFIS) developed for flow characterization inside a cylindrical bubble column reactor.
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Affiliation(s)
- M. Pourtousi
- Department of Mechanical Engineering
- University of Malaya
- 50603 Kuala Lumpur
- Malaysia
| | - Mohammadjavad Zeinali
- Vehicle System Engineering Laboratory
- Malaysia-Japan International Institute of Technology
- Universiti Teknologi Malaysia
- 54100 Kuala Lumpur
- Malaysia
| | - P. Ganesan
- Department of Mechanical Engineering
- University of Malaya
- 50603 Kuala Lumpur
- Malaysia
| | - J. N. Sahu
- Petroleum and Chemical Engineering Programme Area
- Faculty of Engineering
- Institut Teknologi Brunei
- Brunei Darussalam
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Shamshirband S, Petković D, Hashim R, Motamedi S. Adaptive neuro-fuzzy methodology for noise assessment of wind turbine. PLoS One 2014; 9:e103414. [PMID: 25075621 PMCID: PMC4116176 DOI: 10.1371/journal.pone.0103414] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 06/29/2014] [Indexed: 11/18/2022] Open
Abstract
Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
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Affiliation(s)
- Shahaboddin Shamshirband
- Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), Chalous, Mazandaran, Iran
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
- * E-mail:
| | - Dalibor Petković
- University of Niš, Faculty of Mechanical Engineering, Deparment for Mechatronics and Control, Niš, Serbia
| | - Roslan Hashim
- Institute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur, Malaysia
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Shervin Motamedi
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
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