Application of Adaptive Chebyshev and Fast-Fourier Transform to Identify Bradykinesia in Humans from EEG Data Features

Authors

Keywords:

Adaptive Chebyshev, Bradykinesia, Discrete Time - Direct Form Filter, Electroencephalogram (EEG), Fast-Fourier Transform, MATLAB, Neuroscience, Signal Processing

Abstract

Neuroscience is a field that requires utmost meticulousness, and careful dissection of the signals involved. Brain disorders are diverse in nature, and entails one to hold a holistic cognizance of the structure and working of the neurons, along with its signals involved. The Electroencephalogram (EEG) is a data that aids in agnizing the abnormalities, and therefore requires scrupulous analysis through appropriate techniques. Bradykinesia is a neurological disorder which can subsequently lead to Parkinson’s disease, and thus requires explicit observation of any small differences at its early stages through the EEG feature signals. While algorithmic approaches from previous studies have entailed the movements and development of sensory organs, this study pivots on recognizing the abnormality of the EEG data signal for an individual through pre-processing and signal processing methods. This paper pivots on utilizing Fast-Fourier Transform (FFT) and Adaptive Chebyshev using Discrete time-direct form filter to segregate the spectrum bands, the Power Spectral Density (PSD), along with the process of side lobe reduction are proposed in order to unambiguously comprehend the inconsistencies of the frequency amplitude between the normal and abnormal signals. The simulations for this study are carried out in MATLAB, and the results are successfully obtained.

Downloads

Download data is not yet available.

References

D. C. R. Novitasari et al., “Classification of EEG signals using Fast-Fourier transform (FFT) and Adaptive Neuro-fuzzy Inference System (ANFIS),” J. Matemmatika MANTIK, vol. 5, no. 1, pp. 35-44, May 2019, doi:10.15642/mantik.2019.5.1.35-44.

M. C. Guerrero et al., “EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks,” Heliyon, vol. 7, no. 6, Jun., e07258, 2021, doi:10.1016/j.heliyon.2021.e07258.

N. Kumar et al., “Wavelet transform for classification of EEG signal using SVM and ANN,” Biomed. Pharmacol. J., vol. 10, no. 4, pp. 2061-2069, 2017, doi:10.13005/bpj/1328.

C. Jiang et al., “Enhancing EEG-based classification of depression patients using spatial information,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, 566-575, Febr. 2021, doi:10.1109/TNSRE.2021.3059429.

S. K. Satapathy et al., “EEG signal classification using PSO trained RBF neural network for epilepsy identification,” Inform. Med., vol. 6, pp. 1-11. Available at: https://doi.org/10.1016/j.imu.2016.12.001, 2017.

H. Khodakarami et al., “A method for measuring time spent in bradykinesia and dyskinesia in people with Parkinson’s disease using an ambulatory monitor,” J. Neuroeng. Rehabil., vol. 18, no. 1, p. 116, Jul. 2021, doi:10.1186/s12984-021-00905-4.

C. Gao et al., “Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: Clinical validation,” Transl. Neurodegener., vol. 7, p. 18, 2018, ISSN: 2047-9158, doi:10.1186/s40035-018-0124-x.

R. I. Griffiths et al., “Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease,” J. Parkinsons Dis., vol. 2, no. 1, pp. 47-55, 2012, doi:10.3233/JPD-2012-11071.

H.-C. Wang et al., “Impairment of EEG desynchronisation before and during movement and its relation to bradykinesia in Parkinson’s disease,” J. Neurol. Neurosurg. Psychiatry, vol. 66, no. 4, pp. 442-446, 1999, doi:10.1136/jnnp.66.4.442.

O. B. Feodoritova and N. D., “Novikova1 and V. T. Zhukov,” Adapt. Chebyshev Iterative Method, Mathematica Montisnigri, vol. XLIII, 2018.

F. Li et al., “A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning”, 28 February 2020, J. Appl. Sci., vol. 10, p. 2020, 1605, doi:10.3390/app10051605.

J. Sun et al., “A hybrid deep neural network for classifcation of schizophrenia using EEG Data,” Sci. Rep., vol. 11, no. 1, p. 4706, 2021, doi:10.1038/s41598-021-83350-6.

M. Beudel et al., “Parkinson bradykinesia correlates with EEG background frequency and perceptual forward projection,” Parkinsonism Relat. Disord., vol. 21, no. 7, pp. 783-788, 2015 Jul., doi:10.1016/j.parkreldis.2015.05.004.

A. Safaai-Jazi and W. L. Stutzman, “A Fourier method for sidelobe reduction in equally spaced linear arrays,” Radio Sci., vol. 53, no. 4, Apr., pp. 565-576, 2018, doi:10.1002/2017RS006526.

L. Aksoy et al., “Design of digit-serial FIR filters: Algorithms, architectures, and a CAD tool,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 21, no. 3, pp. 498-511, Mar. 2013, doi:10.1109/TVLSI.2012.2188917.

D. Bansal and R. Mahajan, ‘EEG-Based Brain–Computer Interfacing’, Cognitive Analysis and Control Application, 2019, pp. 21-71, doi:10.1016/B978-0-12-814687-3.00002-8.

J. Slavic et al., “Vibration fatigue by spectral methods,” Signal Process. ScienceDirect, pp. 51-74, 2021, doi:10.1016/B978-0-12-822190-7.00009-8.

A. V. Nettem and D. E. Rani, “Modified PWNLFM signal for side lobe reduction,” Int. J. Eng. Technol., 7(4), vols. 4-7, no. v, 2018, doi:10.14419/ijet.V7i4.20.22110.

H. U. Amin et al., “Classification of EEG signals based on pattern recognition approach,” Front. Comp. Neurosci., vol. 11, 103, 2017, doi:10.3389/fncom.2017.00103.

R. Syed Jamalullah and L. M. Gladence, “Implementing clustering methodology by obtaining centroids of sensor nodes for human brain functionality.” 6th International Conference on Advanced Computing & Communication Systems (ICACCS), 2020.

R. Syed Jamalullah et al., “Development of end – To – End encoder – Decoder model applying voice recognition system in different channels,” Int. J. Recent Technol. Eng. ISSN, vol. 8, no. 2, suppl. 11, and Sept. 2019, p. 2277-3878.

R. Syed Jamalullah and L. M. Gladence, “Implementing a non-invasive brain temperature monitoring system with two-type RF switches antenna,” Biochem. Biophys. Res. Commun. special issue, vol. 14, no. 5, pp. 245-247, 2021.

Process flow of the proposed Research

Downloads

Published

17.02.2023

How to Cite

Jamalullah R., S. ., & Mary Gladence, L. . (2023). Application of Adaptive Chebyshev and Fast-Fourier Transform to Identify Bradykinesia in Humans from EEG Data Features. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 119–125. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2602

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.