Information Technology Reference
In-Depth Information
Fig. 6.1: Montage for scalp electrode placement.
As each stimulation lasted for 30 s and a 4-s time window was used to compute
one element of the Lyapunov exponent time series, each stimulation provided seven
data points. Since the EEG patterns of a patient may have been changing throughout
the observed period due to changes in his/her conditions not relevant to the inves-
tigated phenomenon, each of the seven samples across all stimulation cycles were
averaged. Thus, seven Lyapunov exponent samples have been created to represent
the positive class. To create the negative class, 10 Lyapunov exponent data points
were considered 250 s after each stimulation. In the similar way, these 10 samples
were averaged across all stimulation cycles. So, the created negative class contains
10 averaged Lyapunov exponent data samples from nonstimulation time intervals.
Then, the biclustering experiment was done on two 26
17 matrices represent-
ing patients A and B. The patient A data were conditionally biclustering admitting
with respect to given stimulation and nonstimulation classes without excluding any
features. All but one feature were classified into the nonstimulation class, which in-
dicates that for almost all EEG channels the Lyapunov exponent was consistently
decreasing during the stimulation with one channel being the only exception.
Cross-validation was performed for the obtained biclustering by leave-one-out
method examining for each sample whether it would be classified in the appropriate
class if the feature selection was performed without it. It turned out that all classes
of all 17 samples are confirmed by this method.
To make the patient
B
data set conditionally biclustering admitting with respect
to given stimulation and nonstimulation classes only five features were selected. The
one-leave-out experiment classified correctly all but four samples. The biclustering
heatmaps are presented in Fig. 6.2.
The obtained biclustering results allow to assume that signals from certain parts
of the brain consistently change their characteristics when VNS is switched on
and could provide a basis for desirable VNS stimulation parameters. A physiologic
marker of optimal VNS effect could greatly reduce the cost, time, and risk of cal-
ibrating VNS stimulation parameters in newly implanted patients compared to the
current method of clinical response.
×
