Seizure classification using BERT NLP and a comparison of source isolation techniques with two different time-frequency analysis

Epilepsy is one of the most common neurological disorders in the world [1], affecting about 50 million people worldwide [2]. Epileptic seizures occur when millions of neurons are synchronously excited, resulting in a wave of electrical activity in the cerebral cortex [3]. Electroencephalography (EEG) is a noninvasive tool that measures cortical activity with millisecond temporal resolution. EEGs record the electrical potentials generated by the cerebral cortex nerve cells [4]. Therefore, this tool is commonly used for the analysis and detection of seizures [5]. Epilepsy causes many difficulties in relation to the quality of life of the patient. It is therefore vital that automatic detection algorithms exist to aid neurologists to accurately classify the different types of seizures. Roy et al. [10] used different machine learning techniques to achieve an average F1-score of 0.561 using 2 s windows whilst Vanabelle et al. [11] used 1 s windows and achieved an accuracy of 51.33%, which shows that reducing the time window would also decrease the accuracy of classification. This paper aims to show that an NLP can be used for hierarchical classification, following upon an earlier work on combining simple partial and complex partial seizures [9]. The second aim is to show a pipeline that can be used to separate the seizures back into their original labels using neural networks. This method is quick, effective, and requires less training.