Application of time-scale techniques to detection of epileptiform activity in the EEG [article]

Hansjerg Gölz, University Of Canterbury
2012
Epilepsy is a neurological disorder for which the electroencephalogram (EEG) is the most important diagnostic tool. Detection of interictal (between-seizure) epileptiform activity in the EEG is, however, complicated by various artifacts and spike-like transient waveforms in the normal background EEG. Approaches to the automatic detection of epileptiform discharges (EDs) require sophisticated signal analysis methods. We have developed a new system for the detection of EDs spikes, sharp waves,
more » ... spike-and-wave complexes in the EEG. It is based on the continuous wavelet transform (CWT) and incorporates statistical pattern recognition and 3D spatial source analysis. Wavelet-based approaches to signal analysis include the discrete wavelet transform (DWT), the CWT, and matching pursuit. Both DWT and CWT are based on a prototype filter, defined by a wavelet function applied at various scales, that can lead to accurate localization in both the time and frequency domains. Wavelet-based methods are more appropriate for the analysis of non-stationary signals - such as the EEG than the more conventional standard and short-time Fourier transforms. Both the DWT and CWT have been applied previously to the spike detection problem but the CWT with a complex-valued wavelet is considered superior due to translation-invariance and independence from the phase of the transient. Statistical pattern recognition covers a wide range of classification techniques including artificial neural networks and both linear and quadratic discriminant functions. Linear and quadratic discriminant functions perform well on the separation of two non Gaussian distributed samples from multidimensional feature spaces. In many medical diagnostic decision problems there is a large number of healthy controls but only a small number of definitively diagnosed patients. The resulting unequal sample sizes pose a problem for classification but can be counter-balanced by a bias term in the discriminant functions. An alternative interpretation of the output of dis [...]
doi:10.26021/1930 fatcat:l7ngqx2rdfflvlolc5kshfihiq