Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information
1 School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley, WA, 6009, Australia
2 Faculty of Health Science and Biomedical Engineering, Universiti Teknologi Malaysia, 80990, Johor Bahru, Johor, Malaysia
3 University of Queensland Centre for Clinical Research, Building 71/918, Royal Brisbane & Women’s Hospital Campus, Herston, QLD, 4029, Australia
4 College of Engineering, Qatar University, Doha, Qatar
EURASIP Journal on Advances in Signal Processing 2012, 2012:215 doi:10.1186/1687-6180-2012-215Published: 7 October 2012
This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).