Comparison of Three Methods for Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns

• Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features inherent in the EEG as input. The study is based on EEG signals from six full term infants who have suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as area under the curve (AUC) values derived from receiver operating characteristic (ROC) curves for the three methods, and show that the SVM is slightly better than the others, at the cost of a higher computational complexity.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Fysiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Physiology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk laboratorie- och mätteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Laboratory and Measurements Technologies (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering (hsv//eng)

Nyckelord

Biomedical Signal Processing

Classification

Algorithms

Artificial Intelligence

Asphyxia Neonatorum

complications

diagnosis

Brain Damage

Chronic

diagnosis

etiology

Diagnosis

Computer-Assisted

methods

Electroencephalography

methods

Humans

Infant

Newborn

Male

Pattern Recognition

Automated

methods

Reproducibility of Results

Sensitivity and Specificity

Male

Publikations- och innehållstyp

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