Detection of epileptiform activity in the electroencephalogram using artificial neural networks
Christopher J. James · UC Research Repository (University of Canterbury) · 1997
A system for automated detection of epileptiform activity in the electroencephalogram (EEG) has been developed and tested on prerecorded data from a range of patients. Epileptiform activity is manifest as spikes in the EEG and consequently the automated detection of spikes in the EEG is an important tool in the diagnosis of epilepsy and is a goal sought by many researchers. The system presented herein is centred around artificial neural networks (ANNs), in particular the multi-layer perceptron (MLP) and the self-organising feature map (SOFM). The MLP is used in the form of an adaptive filter to enhance the presence of epileptiform transients in the EEG while the SOFM is used to form a novel pattern classifier. A modification to the 'standard' calibration technique for the SOFM is proposed based on a method involving Bayesian probabilities. The SOFM allows a large quantity of EEG data to be used to form a pattern classifier in an unsupervised manner. Fuzzy logic is introduced in order to incorporate spatial contextual information in the spike detection process. By using fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by an electroencephalographer (EEGer) as opposed to a precise biological model. The human brain is overviewed in terms of its structure, organisation and function. Simplistic mathematical modelling of the neural network of the brain is discussed and ANNs are introduced. After reviewing ANNs in general the perceptron based network is introduced and discussed. The SOFM is introduced and through a number of computer simulations several suggestions are put forward regarding the choice of parameters for training the SOFM. After a review of the literature on spike detection systems, in particular ANN based systems, a multi-stage spike detection system is proposed. There are four stages to the system: spike enhancer, mimetic stage, SOFM and fuzzy logic stage. Each stage of the system is discussed at length and measures of performance are indicated at each stage. The importance of spatial and temporal contextual information is discussed and a method using fuzzy logic is proposed to model the spatial reasoning of an EEGer. The system was trained on 35 epileptiform EEGs containing in excess of 3000 epileptiform events and was tested on a different set of 7 EEGs (6 containing epileptiform activity and 1 'normal') containing 133 epileptiform events. The EEGs consisted of standard clinical recordings with an average length of 22.9 minutes. Preliminary results show that the system has a sensitivity of 59% and a selectivity of 31% with an average false detection rate of 61 per hour. The performance compares well with other leading systems to be found in the literature once the measures of performance obtained in each are case placed in context. Several aspects in the system have been identified for modification which should lead to considerable improvements in performance (e.g., temporal context, improved mimetic stage). The new approach to the spike detection problem presented in this thesis shows that it is possible to form an accurate classifier in a self-organised fashion, thus eliminating the need to accurately label large quantities of data - a weak point in many spike detection systems. Furthermore, the importance of spatial contextual analysis is highlighted showing that it is possible to model the spatial reasoning of an EEGer with a fuzzy logic system, thus eliminating the need to produce accurate models of the process.