Patient specific seizure prediction algorithm using Hilbert-Huang Transform
| dc.authorscopusid | 57188729974 | |
| dc.authorscopusid | 57198276954 | |
| dc.authorscopusid | 43262125900 | |
| dc.contributor.author | Duman, Firat | |
| dc.contributor.author | Özdemir, Nilüfer | |
| dc.contributor.author | Yildirim, Esen | |
| dc.date.accessioned | 2024-09-19T15:41:18Z | |
| dc.date.available | 2024-09-19T15:41:18Z | |
| dc.date.issued | 2012 | |
| dc.department | Hatay Mustafa Kemal Üniversitesi | en_US |
| dc.description | IEEE Engineering in Medicine and Biology Society (IEEE-EMBS); Key Lab. Health Informatics, Chin. Acad. Sci. (HI-CAS); CAS-SIAT Institute of Biomedical and Health Engineering (IBHE) | en_US |
| dc.description | IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering -- 2 January 2012 through 7 January 2012 -- Hong Kong and Shenzhen -- 91242 | en_US |
| dc.description.abstract | Epilepsy is a neurological disorder that affects about 50 million people around the world. EEG signal processing plays an important role in detection and prediction of epileptic seizures. The aim of this study is to develop a method for early seizure prediction based on Hilbert-Huang Transform. In this patient specific method, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) and first 5 IMFs are used to obtain features for classification of preictal and interictal recordings. Proposed method was tested on Freiburg EEG database. A total of 58 hours of preictal data, prior to 87 seizures, and 490 hours of interictal data were examined. Algorithm resulted in 89.66% sensitivity (78 of 87 seizures) and 0.49 FPs/h using 30 seconds EEG segment with 50% overlap. © 2012 IEEE. | en_US |
| dc.identifier.doi | 10.1109/BHI.2012.6211680 | |
| dc.identifier.endpage | 708 | en_US |
| dc.identifier.isbn | 978-145772177-9 | |
| dc.identifier.scopus | 2-s2.0-84864186967 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.startpage | 705 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/BHI.2012.6211680 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12483/14169 | |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Algorithms | en_US |
| dc.subject | Biomedical equipment | en_US |
| dc.subject | Biosensors | en_US |
| dc.subject | Neurology | en_US |
| dc.subject | EEG signal processing | en_US |
| dc.subject | EEG signals | en_US |
| dc.subject | Epileptic seizures | en_US |
| dc.subject | Hilbert Huang transforms | en_US |
| dc.subject | Intrinsic Mode functions | en_US |
| dc.subject | Neurological disorders | en_US |
| dc.subject | Patient specific | en_US |
| dc.subject | Seizure prediction | en_US |
| dc.subject | Signal processing | en_US |
| dc.title | Patient specific seizure prediction algorithm using Hilbert-Huang Transform | en_US |
| dc.type | Conference Object | en_US |
