A review of time-frequency matched filter design with application to seizure detection in multichannel newborn EEG

Abstract

This paper presents a novel design of a time-frequency (t-f) matched filter as a solution to the problem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newborn seizure using multichannel EEG signals. The solution reduces to two possible t-f approaches that use a general formulation of t-f matched filters (TFMFs) based on the Wigner-Ville and cross Wigner-Ville distributions, and a third new approach based on the signal ambiguity domain representation; referred to as Radon-ambiguity detector. This contribution defines a general design formulation and then implements it for newborn seizure detection using multichannel EEG signals. Finally, the performance of different TFMFs is evaluated for different t-f kernels in terms of classification accuracy using real newborn EEG signals. Experimental results show that the detection method which uses TFMFs based on the cross Wigner-Ville distribution outperforms other approaches including the existing TFMF-based ones. The results also show that TFMFs which use high-resolution kernels such as the modified B-distribution, achieve higher detection accuracies compared to the ones which use other reduced-interference t-f kernels.