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.
Highlights
Seizures are among the most common and important signs of acute newborn encephalopathy
Since the aim of the paper is to show the added value of multi-channel EEG based seizure detections over the single channel EEG based seizure detection, we have used a wide range of features that were derived in previous studies on EEG
Two classification approaches based on multi-channel feature fusion and multi-channel decision fusion have been introduced in order to exploit EEG information from simultaneously recorded multiple EEG channels to detect newborn seizure
Summary
Seizures are among the most common and important signs of acute newborn encephalopathy (degenerative disease of the brain). These steps include data acquisition, preprocessing of the EEG to remove unwanted noise, extracting time, frequency and TF/TS domain features from the multi-channel EEG, selecting non-redundant and discriminative EEG features from the larger extracted set and fusing the selected EEG features at two different levels as mentioned above. Both proposed EEG multi-channel fusion approaches consist of a sequence of processing steps, namely; preprocessing of the EEG signal, extraction of the EEG features and selection of optimal feature subset from the larger set.
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