Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques
Diagnostic and warning methods can prove useful for epilepsy infinite recognition, controlling seizure (to prepare for seizure e.g., pull over if driving) and organizing medicine schedule to reduce unwanted side effects of untimely medication. Such methods employ brain electrical activity signals ca...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Faculty of Engineering, Universiti Putra Malaysia
2012
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/50688/ http://psasir.upm.edu.my/id/eprint/50688/1/_TechnicalPapers_CAFEi2012_202.pdf |
| _version_ | 1848851626025025536 |
|---|---|
| author | Khosropanah, Pegah Ramli, Abd Rahman Ashurov, Rashvan Ahmedov, Anvarjon |
| author_facet | Khosropanah, Pegah Ramli, Abd Rahman Ashurov, Rashvan Ahmedov, Anvarjon |
| author_sort | Khosropanah, Pegah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Diagnostic and warning methods can prove useful for epilepsy infinite recognition, controlling seizure (to prepare for seizure e.g., pull over if driving) and organizing medicine schedule to reduce unwanted side effects of untimely medication. Such methods employ brain electrical activity signals called electro encephalography (EEG). Epileptiform from EEG can be detected either visually (by specialist inspection) or automatically (by using signal processing knowledge). The first method requires plenty of time and precision. Automatic systems, growingly popular in recent decades, have been proposed to reduce time. In this study, an automated system is developed to detect spikes from EEG and classify them into healthy and epileptic categories in order to increase accuracy and precision. Discrete wavelet (DWT) is applied as a feature extraction method and adaptive neuro-fuzzy inference system (ANFIS) is used for classification. A sensitivity of 99% has been obtained. |
| first_indexed | 2025-11-15T10:25:10Z |
| format | Conference or Workshop Item |
| id | upm-50688 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:25:10Z |
| publishDate | 2012 |
| publisher | Faculty of Engineering, Universiti Putra Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-506882017-03-02T06:20:35Z http://psasir.upm.edu.my/id/eprint/50688/ Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques Khosropanah, Pegah Ramli, Abd Rahman Ashurov, Rashvan Ahmedov, Anvarjon Diagnostic and warning methods can prove useful for epilepsy infinite recognition, controlling seizure (to prepare for seizure e.g., pull over if driving) and organizing medicine schedule to reduce unwanted side effects of untimely medication. Such methods employ brain electrical activity signals called electro encephalography (EEG). Epileptiform from EEG can be detected either visually (by specialist inspection) or automatically (by using signal processing knowledge). The first method requires plenty of time and precision. Automatic systems, growingly popular in recent decades, have been proposed to reduce time. In this study, an automated system is developed to detect spikes from EEG and classify them into healthy and epileptic categories in order to increase accuracy and precision. Discrete wavelet (DWT) is applied as a feature extraction method and adaptive neuro-fuzzy inference system (ANFIS) is used for classification. A sensitivity of 99% has been obtained. Faculty of Engineering, Universiti Putra Malaysia 2012 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/50688/1/_TechnicalPapers_CAFEi2012_202.pdf Khosropanah, Pegah and Ramli, Abd Rahman and Ashurov, Rashvan and Ahmedov, Anvarjon (2012) Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques. In: International Conference on Agricultural and Food Engineering for Life (Cafei2012), 26-28 Nov. 2012, Palm Garden Hotel, Putrajaya. (pp. 583-590). http://cafei.upm.edu.my/download.php?filename=/TechnicalPapers/CAFEi2012_202.pdf |
| spellingShingle | Khosropanah, Pegah Ramli, Abd Rahman Ashurov, Rashvan Ahmedov, Anvarjon Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques |
| title | Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques |
| title_full | Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques |
| title_fullStr | Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques |
| title_full_unstemmed | Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques |
| title_short | Detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(ANFIS) techniques |
| title_sort | detection of epileptic spikes in egg signal using wavelet transform and adaptive neuro – fuzzy inference system(anfis) techniques |
| url | http://psasir.upm.edu.my/id/eprint/50688/ http://psasir.upm.edu.my/id/eprint/50688/ http://psasir.upm.edu.my/id/eprint/50688/1/_TechnicalPapers_CAFEi2012_202.pdf |