Seizure prediction and control in epilepsy
Creators: name: Bruzzo, Angela <1979>
Publisher: name: Alma Mater Studiorum - Università di Bologna
Year: 2008
Subjects: M-PSI/02 Psicobiologia e psicologia fisiologica

description: The first part of my thesis presents an overview of the different approaches used in the past two decades in the
attempt to forecast epileptic seizure on the basis of intracranial and scalp EEG. Past research could reveal some value of
linear and nonlinear algorithms to detect EEG features changing over different phases of the epileptic cycle. However,
their exact value for seizure prediction, in terms of sensitivity and specificity, is still discussed and has to be evaluated.
In particular, the monitored EEG features may fluctuate with the vigilance state and lead to false alarms. Recently, such
a dependency on vigilance states has been reported for some seizure prediction methods, suggesting a reduced
reliability. An additional factor limiting application and validation of most seizure-prediction techniques is their
computational load. For the first time, the reliability of permutation entropy [PE] was verified in seizure prediction on
scalp EEG data, contemporarily controlling for its dependency on different vigilance states. PE was recently introduced
as an extremely fast and robust complexity measure for chaotic time series and thus suitable for online application even
in portable systems. The capability of PE to distinguish between preictal and interictal state has been demonstrated
using Receiver Operating Characteristics (ROC) analysis. Correlation analysis was used to assess dependency of PE on
vigilance states. Scalp EEG-Data from two right temporal epileptic lobe (RTLE) patients and from one patient with
right frontal lobe epilepsy were analysed. The last patient was included only in the correlation analysis, since no
datasets including seizures have been available for him. The ROC analysis showed a good separability of interictal and
preictal phases for both RTLE patients, suggesting that PE could be sensitive to EEG modifications, not visible on
visual inspection, that might occur well in advance respect to the EEG and clinical onset of seizures. However, the
simultaneous assessment of the changes in vigilance showed that: a) all seizures occurred in association with the
transition of vigilance states; b) PE was sensitive in detecting different vigilance states, independently of seizure
occurrences. Due to the limitations of the datasets, these results cannot rule out the capability of PE to detect preictal
states. However, the good separability between pre- and interictal phases might depend exclusively on the coincidence
of epileptic seizure onset with a transition from a state of low vigilance to a state of increased vigilance. The finding of
a dependency of PE on vigilance state is an original finding, not reported in literature, and suggesting the possibility to
classify vigilance states by means of PE in an authomatic and objectic way.
The second part of my thesis provides the description of a novel behavioral task based on motor imagery skills,
firstly introduced (Bruzzo et al. 2007), in order to study mental simulation of biological and non-biological movement
in paranoid schizophrenics (PS). Immediately after the presentation of a real movement, participants had to imagine or
re-enact the very same movement. By key release and key press respectively, participants had to indicate when they
started and ended the mental simulation or the re-enactment, making it feasible to measure the duration of the simulated
or re-enacted movements. The proportional error between duration of the re-enacted/simulated movement and the
template movement were compared between different conditions, as well as between PS and healthy subjects. Results
revealed a double dissociation between the mechanisms of mental simulation involved in biological and non-biologial
movement simulation. While for PS were found large errors for simulation of biological movements, while being more
acurate than healthy subjects during simulation of non-biological movements. Healthy subjects showed the opposite
relationship, making errors during simulation of non-biological movements, but being most accurate during simulation
of non-biological movements. However, the good timing precision during re-enactment of the movements in all
conditions and in both groups of participants suggests that perception, memory and attention, as well as motor control
processes were not affected. Based upon a long history of literature reporting the existence of psychotic episodes in
epileptic patients, a longitudinal study, using a slightly modified behavioral paradigm, was carried out with two RTLE
patients, one patient with idiopathic generalized epilepsy and one patient with extratemporal lobe epilepsy. Results
provide strong evidence for a possibility to predict upcoming seizures in RTLE patients behaviorally. In the last part of
the thesis it has been validated a behavioural strategy based on neurobiofeedback training, to voluntarily control
seizures and to reduce there frequency. Three epileptic patients were included in this study. The biofeedback was based
on monitoring of slow cortical potentials (SCPs) extracted online from scalp EEG. Patients were trained to produce
positive shifts of SCPs. After a training phase patients were monitored for 6 months in order to validate the ability of
the learned strategy to reduce seizure frequency. Two of the three refractory epileptic patients recruited for this study
showed improvements in self-management and reduction of ictal episodes, even six months after the last training
session.