Automated neonatal seizure detection mimicking a human observer reading EEG.
Clin Neurophysiol
; 119(11): 2447-54, 2008 Nov.
Article
in En
| MEDLINE
| ID: mdl-18824405
ABSTRACT
OBJECTIVE:
The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant.METHODS:
We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation.RESULTS:
The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour.CONCLUSIONS:
Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms.SIGNIFICANCE:
The proposed algorithm significantly improves neonatal seizure detection and monitoring.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Seizures
/
Diagnosis, Computer-Assisted
/
Electroencephalography
/
Infant, Newborn, Diseases
Type of study:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
/
Infant
/
Newborn
Language:
En
Journal:
Clin Neurophysiol
Journal subject:
NEUROLOGIA
/
PSICOFISIOLOGIA
Year:
2008
Type:
Article
Affiliation country:
Belgium