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D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J. and Litt, B. (2003) Epileptic Seizure Prediction Using Hybrid Feature Selection over Multiple Intracranial EEG Electrode Contacts: A Report of Four Patients. IEEE Transactions on Biomedical Engineering, 50, 603-615.
http://dx.doi.org/10.1109/tbme.2003.810706

has been cited by the following article:

• TITLE: A Multi-Channel Fusion Based Newborn Seizure Detection

  AUTHORS: Malarvili BalaKrishnan, Paul Colditz, Boualeum Boashash

  KEYWORDS: EEG, Newborn Seizure Detection, Multi-Channel, Feature Fusion, Decision/Classifier Fusion

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.7 No.8, June 26, 2014

  ABSTRACT: We propose and compare two multi-channel fusion schemes to utilize the information extracted from simultaneously recorded multiple newborn electroencephalogram (EEG) channels for seizure detection. The first approach is known as the multi-channel feature fusion. It involves concatenating EEG feature vectors independently obtained from the different EEG channels to form a single feature vector. The second approach, called the multi-channel decision/classifier fusion, is achieved by combining the independent decisions of the different EEG channels to form an overall decision as to the existence of a newborn EEG seizure. The first approach suffers from the large dimensionality problem. In order to overcome this problem, three different dimensionality reduction techniques based on the sum, Fisher’s linear discriminant and symmetrical uncertainty (SU) were considered. It was found that feature fusion based on SU technique outperformed the other two techniques. It was also shown that feature fusion, which was developed on the basis that there was inter-dependence between recorded EEG channels, was superior to the independent decision fusion.