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Abstract

Time series representing the brain activity of epileptic patients during a seizure can be mapped into sound to highlight the main properties and different phases. We apply sonification strategies and techniques of quantum computing to model seizure events and shape more general approaches towards prediction. We consider here pre-surgery electrocorticography of patients not responding to pharmacological therapy. Seizure time series are mapped to sound, yielding a polyphonic sequence. Then, we propose two quantum approaches to simulate a similar episode of seizure, and we sonify the results. The comparison of sonifications can give hints on similarities and discrepancies between real data and simulations, helping refine the in silico model. This pioneering approach can foster new directions of real-data investigation, helping define a new test bench for analysis and prediction of seizures.