Adverse Events Prediction
The objective of this research project, conducted in collaboration with the Policlinico Gemelli, is the development of an automatic system for the analysis of EEG (ElectroEncephaloGram) signals in order to predict seizures in patients resistant to drug treatment.
The solution requires the capture and recognition of EEG signal changes typical of the onset of a pre-critical phase. This allows the necessary precautions to be activated to avoid or manage the actual seizure. One of the difficulties to be faced in the development of an automated solution is the strong noise of the EEG signals.
We filter the EEG signals managing to extract the most relevant features through autoregressive models (AR). Based on the features thus highlighted we build classification models of type Support Vector Machines (SVM), which we train to distinguish between a normal phase and a pre-critical phase.
Results and Benefits
The predictive model helps to identify the pre-critical phase of a seizure.