Abstract
Psychogenic non-epileptic seizures (PNES) are characterised by a lack of epileptic activity in
the brain and about one in five referrals to epilepsy clinics actually have this condition. PNES
is diagnosed by recording a seizure using video-electroencephalogram (EEG), from which an
expert inspects the semiology and the EEG. This method is reliable but expensive, inconvenient
for the patient, and not accessible for all hospitals. This could be improved with machine
learning classifiers, which are models that can consider multiple inputs at once. Since no single
biomarker has been found to diagnose PNES, these classifiers could be a very useful aid to
clinicians.
The hypothesis of this PhD is that is it possible to identify subjects with PNES from those
with epilepsy using non-ictal biomedical signals with machine learning. This was tested using
a data set of interictal and preictal EEG and electrocardiograms (ECG) recordings from 48
subjects with PNES and 29 subjects with epilepsy. A wide range of features were extracted from
the signals and grouped into ‘families’. The performance of the different feature families was
evaluated using statistical methods and ranked using two feature ranking methods. The families
and the signals themselves were then classified using several machine learning models.
The highest classification accuracy reported from purely statistical analysis was 60.67%. The
highest balanced accuracy reported by the machine learning models, however, was 97.00%
from the ‘all-reduced’ family. Therefore, machine learning was much more effective than using
individual features. This PhD has therefore shown that machine learning could be a powerful
aid in PNES diagnosis, which can limit the costs by reducing the need for diagnosis with a video
recording and review by an expert.