A classification on brain wave patterns for Parkinson’s patients using WEKA

Nurshuhada Mahfuz, Waidah Ismail, Nor Azila Noh, Mohd Zalisham Jali, Dalilah Abdullah, Md. Jan Nordin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, classification of brain wave using real-world data from Parkinson’s patients in producing an emotional model is presented. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aims to find the “best” classification for brain wave patterns in patients with Parkinson’s disease. This work performed is based on the four phases, which are first phase is raw data and after data processing using statistical features such as mean and standard deviation. The second phase is the sum of hertz, the third is the sum of hertz divided by the number of hertz, and last is the sum of hertz divided by total hertz. We are using five attributes that are patients, class, domain, location, and hertz. The data were classified using WEKA. The results showed that BayesNet gave a consistent result for all the phases from multilayer perceptron and K-Means. However, K-Mean gave the highest result in the first phase. Our results are based on a real-world data from Parkinson’s patients.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages21-33
Number of pages13
Volume355
ISBN (Print)9783319173979
DOIs
Publication statusPublished - 2015
Event4th World Congress on Information and Communication Technologies, WICT 2014 - Malacca, Malaysia
Duration: 8 Dec 201411 Dec 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume355
ISSN (Print)21945357

Other

Other4th World Congress on Information and Communication Technologies, WICT 2014
CountryMalaysia
CityMalacca
Period8/12/1411/12/14

Fingerprint

Brain
Multilayer neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Mahfuz, N., Ismail, W., Noh, N. A., Jali, M. Z., Abdullah, D., & Nordin, M. J. (2015). A classification on brain wave patterns for Parkinson’s patients using WEKA. In Advances in Intelligent Systems and Computing (Vol. 355, pp. 21-33). (Advances in Intelligent Systems and Computing; Vol. 355). Springer Verlag. https://doi.org/10.1007/978-3-319-17398-6_3

A classification on brain wave patterns for Parkinson’s patients using WEKA. / Mahfuz, Nurshuhada; Ismail, Waidah; Noh, Nor Azila; Jali, Mohd Zalisham; Abdullah, Dalilah; Nordin, Md. Jan.

Advances in Intelligent Systems and Computing. Vol. 355 Springer Verlag, 2015. p. 21-33 (Advances in Intelligent Systems and Computing; Vol. 355).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mahfuz, N, Ismail, W, Noh, NA, Jali, MZ, Abdullah, D & Nordin, MJ 2015, A classification on brain wave patterns for Parkinson’s patients using WEKA. in Advances in Intelligent Systems and Computing. vol. 355, Advances in Intelligent Systems and Computing, vol. 355, Springer Verlag, pp. 21-33, 4th World Congress on Information and Communication Technologies, WICT 2014, Malacca, Malaysia, 8/12/14. https://doi.org/10.1007/978-3-319-17398-6_3
Mahfuz N, Ismail W, Noh NA, Jali MZ, Abdullah D, Nordin MJ. A classification on brain wave patterns for Parkinson’s patients using WEKA. In Advances in Intelligent Systems and Computing. Vol. 355. Springer Verlag. 2015. p. 21-33. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-17398-6_3
Mahfuz, Nurshuhada ; Ismail, Waidah ; Noh, Nor Azila ; Jali, Mohd Zalisham ; Abdullah, Dalilah ; Nordin, Md. Jan. / A classification on brain wave patterns for Parkinson’s patients using WEKA. Advances in Intelligent Systems and Computing. Vol. 355 Springer Verlag, 2015. pp. 21-33 (Advances in Intelligent Systems and Computing).
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