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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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  1/2014 - 20

 HIGH-IMPACT PAPER 

Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

GEMAN, O. See more information about GEMAN, O. on SCOPUS See more information about GEMAN, O. on IEEExplore See more information about GEMAN, O. on Web of Science, COSTIN, H. See more information about COSTIN, H. on SCOPUS See more information about COSTIN, H. on SCOPUS See more information about COSTIN, H. on Web of Science
 
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Download PDF pdficon (741 KB) | Citation | Downloads: 1,068 | Views: 4,790

Author keywords
adaptive neuro-fuzzy classifier, artificial neural networks, handwriting analysis, nonlinear dynamics, tremor

References keywords
inson(11), disease(10), system(6), tremor(5), geman(5), analysis(5), processing(4), link(4), data(4), costin(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 133 - 138
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01020
Web of Science Accession Number: 000332062300020
SCOPUS ID: 84894623357

Abstract
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Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD) is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP) and an Adaptive Neuro-Fuzzy Classifier (ANFC). In general, the results may be expressed as a prognostic (risk degree to contact PD).


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Cited-By CrossRef

[1] Parkinson’s Disease in Romania: A Scoping Review, Rosca, Elena Cecilia, Tudor, Raluca, Cornea, Amalia, Simu, Mihaela, Brain Sciences, ISSN 2076-3425, Issue 6, Volume 11, 2021.
Digital Object Identifier: 10.3390/brainsci11060709
[CrossRef]

[2] Mathematical model for early stage identification of Parkinson’s disease using neurotransmitter: GABA, Anita, S., Arokiadass, R., International Journal of Information Technology, ISSN 2511-2104, Issue 1, Volume 14, 2022.
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[CrossRef]

[3] Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring, di Biase, Lazzaro, Pecoraro, Pasquale Maria, Pecoraro, Giovanni, Caminiti, Maria Letizia, Di Lazzaro, Vincenzo, Sensors, ISSN 1424-8220, Issue 21, Volume 22, 2022.
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[4] A Brief Review on the Validity and Reliability of Microsoft Kinect Sensors for Functional Assessment Applications, DIAZ-MONTERROSAS, P. R., POSADA-GOMEZ, R., MARTINEZ-SIBAJA, A., AGUILAR-LASSERRE, A. A., JUAREZ-MARTINEZ, U., TRUJILLO-CABALLERO, J. C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 18, 2018.
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[5] Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach, Moon, Sanghee, Song, Hyun-Je, Sharma, Vibhash D., Lyons, Kelly E., Pahwa, Rajesh, Akinwuntan, Abiodun E., Devos, Hannes, Journal of NeuroEngineering and Rehabilitation, ISSN 1743-0003, Issue 1, Volume 17, 2020.
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[6] Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population, Cicirelli, Grazia, Marani, Roberto, Petitti, Antonio, Milella, Annalisa, D’Orazio, Tiziana, Sensors, ISSN 1424-8220, Issue 10, Volume 21, 2021.
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[7] Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint–Node Plots, Lee, Posen, Chen, Tai-Been, Wang, Chi-Yuan, Hsu, Shih-Yen, Liu, Chin-Hsuan, Sensors, ISSN 1424-8220, Issue 9, Volume 21, 2021.
Digital Object Identifier: 10.3390/s21093212
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[8] Mobile@Old: A Smart Home Platform for Enhancing the Elderly Mobility, MOCANU, I., SCHPOR, O.-A., CRAMARIUC, B., RUSU, L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 17, 2017.
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[10] A Novel Method of Recognizing Disturbance Events in Φ-OTDR Based on Affinity Propagation Clustering and Perturbation Signal Selection, Xu, Shaohua, Qin, Zujun, Zhang, Wentao, Xiong, Xianming, Li, Heng, Wei, Zexian, Postolache, Octavian Adrian, Mi, Chao, IEEE Sensors Journal, ISSN 1530-437X, Issue 12, Volume 21, 2021.
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[11] Implementation of High Speed Tangent Sigmoid Transfer Function Approximations for Artificial Neural Network Applications on FPGA, KOYUNCU, I., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 18, 2018.
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[12] k-Degree Anonymity Model for Social Network Data Publishing, MACWAN, K. R., PATEL, S. J., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 17, 2017.
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[13] Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease, Drotár, Peter, Mekyska, Jiří, Rektorová, Irena, Masarová, Lucia, Smékal, Zdenek, Faundez-Zanuy, Marcos, Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue 3, Volume 117, 2014.
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[14] Using Deep Learning for Task and Tremor Type Classification in People with Parkinson’s Disease, Farhani, Ghazal, Zhou, Yue, Jenkins, Mary E., Naish, Michael D., Trejos, Ana Luisa, Sensors, ISSN 1424-8220, Issue 19, Volume 22, 2022.
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[15] Diagnosis of Alzheimer's Disease from Brain Magnetic Resonance Imaging Images using Deep Learning Algorithms, SUGANTHE, R. C., LATHA, R. S., GEETHA, M., SREEKANTH, G. R., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 20, 2020.
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[16] Tremor analysis in neurological disorders using intelligent clothes, Hagan, Marius, Constantinescu, Aurora, Geman, Oana, 2015 E-Health and Bioengineering Conference (EHB), ISBN 978-1-4673-7544-3, 2015.
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[17] Deep brain stimulation efficiency and Parkinson's disease stage prediction using Markov models, Geman, Oana, Chiuchisan, Iuliana, 2015 E-Health and Bioengineering Conference (EHB), ISBN 978-1-4673-7544-3, 2015.
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[18] Adaptive segmentation of multimodal polysomnography data for sleep stages detection, Prochazka, Ales, Kuchynka, Jiri, Yadollahi, Mohammadreza, Araujo, Carmen Paz Suarez, Vysata, Oldrich, 2017 22nd International Conference on Digital Signal Processing (DSP), ISBN 978-1-5386-1895-0, 2017.
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[19] A comparison between healthy and neurological disorders patients using nonlinear dynamic tools, Aldea, Roxana Toderean, Geman, Oana, Chiuchisan, Iuliana, Lazar, Anca Mihaela, 2016 International Conference and Exposition on Electrical and Power Engineering (EPE), ISBN 978-1-5090-6129-7, 2016.
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[20] Real-time health status monitoring system based on a fuzzy agent model, Ivascu, Todor, Aritoni, Ovidiu, 2015 E-Health and Bioengineering Conference (EHB), ISBN 978-1-4673-7544-3, 2015.
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[21] Automatic analysis of the fetal heart rate variability and uterine contractions, Rotariu, Cristian, Pasarica, Alexandru, Andruseac, Gladiola, Costin, Hariton, Nemescu, Dragos, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), ISBN 978-1-4799-5849-8, 2014.
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[22] Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction, Geman, Oana, Chiuchisan, Iuliana, Toderean, Roxana, 2017 E-Health and Bioengineering Conference (EHB), ISBN 978-1-5386-0358-1, 2017.
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[23] Joint EEG — EMG signal processing for identification of the mental tasks in patients with neurological diseases, Geman, Oana, Chiuchisan, Iuliana, Covasa, Mihai, Eftaxias, Konstantinos, Sanei, Saeid, Madeira, Jonni Guiller Ferreira, Boloy, Ronney Arismel Mancebo, 2016 24th European Signal Processing Conference (EUSIPCO), ISBN 978-0-9928-6265-7, 2016.
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[24] NeuroParkinScreen — A health care system for Neurological Disorders Screening and Rehabilitation, Chiuchisan, Iuliana, Geman, Oana, Chiuchisan, Iulian, Iuresi, Andrei Coriolan, Graur, Adrian, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), ISBN 978-1-4799-5849-8, 2014.
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