Sonified Quantum Seizures. Sonification of time series in epileptic seizures and simulation of seizures via quantum modelling

2025-12-22

AI Summary

This study addresses the limited comparability between real neural signals and simulated outputs in epilepsy seizure modeling. We propose a novel “audio–quantum co-analysis” paradigm: first, ECoG time-series signals are converted into multi-voice audibilized audio; second, two variational quantum circuit models simulate seizure dynamics while concurrently generating their audibilized representations; finally, acoustic feature comparison—including spectral entropy and time-frequency structural similarity—quantitatively identifies similarities and discrepancies between empirical and synthetic signals, thereby guiding silicon-based model refinement. This work pioneers the integration of auditory sonification and quantum computing for neuroscience, establishing a perceptible, evaluable benchmark framework tailored to epilepsy mechanism elucidation and predictive validation. It introduces an intuitive, cross-modal verification dimension for neural dynamical modeling, enhancing interpretability and quantitative assessment of computational neurophysiological models.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Quantum Machine Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

We apply sonification strategies and quantum computing to the analysis of an episode of seizure. We first sonify the signal from a selection of channels (from real ECoG data), obtaining 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 extit{in silico} model. This is a pioneering approach, showing how the combination of quantum computing and sonification can broaden the perspective of real-data investigation, and helping define a new test bench for analysis and prediction of seizures.

Problem

• Sonifying epileptic seizure signals for analysis
• Using quantum models to simulate seizure episodes
• Comparing sonifications to refine computational seizure models

Innovation

• Sonify real seizure signals into polyphonic audio sequences
• Simulate seizures using two quantum computing modeling approaches
• Compare audio outputs to refine computational seizure models