Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance
- Authors
- Lee, Sang-Hong; Lim, Joon S.; Kim, Jae-Kwon; Yang, Junggi; Lee, Youngho
- Issue Date
- Aug-2014
- Publisher
- ELSEVIER IRELAND LTD
- Keywords
- Epileptic seizure; Feature selection; Phase space reconstruction; Euclidean distance; ROC curve
- Citation
- COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v.116, no.1, pp.10 - 25
- Journal Title
- COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Volume
- 116
- Number
- 1
- Start Page
- 10
- End Page
- 25
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12397
- DOI
- 10.1016/j.cmpb.2014.04.012
- ISSN
- 0169-2607
- Abstract
- This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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