Classifier Cascade to Aid in Detection of Epileptiform Transients in Interictal EEG

Abstract

The presence of Epileptiform Transients (ET) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. Automated ET detection can increase the uniformity and speed of ET detection. Current ET detection methods suffer from insufficient precision and high false positive rates. Since ETs occur infrequently in the EEG of most patients, the majority of recordings comprise background EEG waveforms. In this work we establish a method to exclude as much background data as possible from EEG recordings by applying a classifier cascade. The remaining data can then be classified using other ET detection methods. We compare a single Support Vector Machine (SVM) to a cascade of SVMs for detecting ETs. Our results show that the precision and false positive rate improve significantly by incorporating a classifier cascade before ET detection. Our method can help improve the precision and false positive rate of an ET detection system. At a fixed sensitivity, we were able to improve precision by 6.78%; and at a fixed false positive rate, the sensitivity improved by 2.83%.

Deep learning for EEG analysis

“…Ultimately, the LS-SVM surpassed all other ML classifiers tested in this dataset. Using a cascade of SVMs rather than a single one also led to increased precision and sensitivity [Bagheri et al, 2018]. This was congruent with the previously mentioned study where affinity propagation beat k-means, leading to the conclusion that updated versions of traditional ML algorithms can improve IED detection [Thomas et al, 2017].…”

“…Another widely used ML classifier is the support vector machine (SVM) [Acır and Güzeliş, 2004, Acir et al, 2005, Bagheri et al, 2018, Chavakula et al, 2013, Guler and Ubeyli, 2007, Iscan et al, 2011, Kelleher et al, 2010, Lima et al, 2010, Thomas et al, 2018. Given the training data, SVMs try to build a barrier (hyperplane) between each class, maximizing the margin between classes.…”