An automatic patient-specific seizure onset detection method using intracranial electroencephalography.
Neuromodulation
; 18(2): 79-84; discussion 84, 2015 Feb.
Article
em En
| MEDLINE
| ID: mdl-25113135
ABSTRACT
OBJECTIVE:
This study presents a multichannel patient-specific seizure detection method based on the empirical mode decomposition (EMD) and support vector machine (SVM) classifier. MATERIALS ANDMETHODS:
The EMD is used to extract features from intracranial electroencephalography (EEG). A machine-learning algorithm is used as a classifier to discriminate between seizure and nonseizure intracranial EEG epochs. A postprocessing algorithm is proposed to reject artifacts and increase the robustness of the method. The proposed method was evaluated using 463 hours of intracranial EEG recordings from 17 patients with a total of 51 seizures in the Freiburg EEG database.RESULTS:
The proposed method had better performance than most of the existing seizure detection systems, including an average sensitivity of 92%, false detection rate (FDR) of 0.17/hour, and time delay (TD) of 12 sec. Moreover, the FDR could be further reduced by a TD extension.CONCLUSIONS:
Given its high sensitivity and low FDR, the proposed patient-specific seizure detection method can greatly assist clinical staff with automatically marking seizures in long-term EEG or detecting seizure onset online with high performance. Early and accurate seizure detection using this method may serve as a practical tool for planning epilepsy interventions.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Convulsões
/
Algoritmos
/
Processamento Eletrônico de Dados
Tipo de estudo:
Diagnostic_studies
Limite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Neuromodulation
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
China