Automatic seizure detection in long-term scalp EEG using an adaptive thresholding technique: a validation study for clinical routine.
Clin Neurophysiol
; 125(7): 1346-52, 2014 Jul.
Article
em En
| MEDLINE
| ID: mdl-24462506
ABSTRACT
OBJECTIVE:
In a previous study we proposed a robust method for automatic seizure detection in scalp EEG recordings. The goal of the current study was to validate an improved algorithm in a much larger group of patients in order to show its general applicability in clinical routine.METHODS:
For the detection of seizures we developed an algorithm based on Short Time Fourier Transform, calculating the integrated power in the frequency band 2.5-12 Hz for a multi-channel seizure detection montage referenced against the average of Fz-Cz-Pz. For identification of seizures an adaptive thresholding technique was applied. Complete data sets of each patient were used for analyses for a fixed set of parameters.RESULTS:
159 patients (117 temporal-lobe epilepsies (TLE), 35 extra-temporal lobe epilepsies (ETLE), 7 other) were included with a total of 25,278 h of EEG data, 794 seizures were analyzed. The sensitivity was 87.3% and number of false detections per hour (FpH) was 0.22/h. The sensitivity for TLE patients was 89.9% and FpH=0.19/h; for ETLE patients sensitivity was 77.4% and FpH=0.25/h.CONCLUSIONS:
The seizure detection algorithm provided high values for sensitivity and selectivity for unselected large EEG data sets without a priori assumptions of seizure patterns.SIGNIFICANCE:
The algorithm is a valuable tool for fast and effective screening of long-term scalp EEG recordings.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Convulsões
/
Algoritmos
/
Cuidados Pré-Operatórios
/
Eletroencefalografia
/
Epilepsia
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Clin Neurophysiol
Assunto da revista:
NEUROLOGIA
/
PSICOFISIOLOGIA
Ano de publicação:
2014
Tipo de documento:
Article