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

Maria Mannone, Paulo Vitor Itaborai, Omar Costa Hamido, Miriam Goldack, Norbert Marwan, Peppino Fazio, Patrizia Ribino

TL;DR

Combines sonification of ECoG time series with quantum-inspired modeling to study epileptic seizures. Uses real patient data for audio representation and two quantum approaches—QPAM-based moment extraction and a time-dependent Ising model—to generate augmented sonic features. Compares real and simulated sonifications to refine in silico seizure models and proposes a new test bench for seizure prediction. Demonstrates a novel cross-disciplinary framework melding auditory display, quantum computation, and neurophysiology.

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 \textit{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.

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

TL;DR

Combines sonification of ECoG time series with quantum-inspired modeling to study epileptic seizures. Uses real patient data for audio representation and two quantum approaches—QPAM-based moment extraction and a time-dependent Ising model—to generate augmented sonic features. Compares real and simulated sonifications to refine in silico seizure models and proposes a new test bench for seizure prediction. Demonstrates a novel cross-disciplinary framework melding auditory display, quantum computation, and neurophysiology.

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 \textit{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.
Paper Structure (11 sections, 7 equations, 7 figures)

This paper contains 11 sections, 7 equations, 7 figures.

Table of Contents

  1. Introduction
  2. A case study of time series and the sonification
  3. Sonification of the epilepsy data
  4. Enhancing the Sonification by Integrating quantum algorithms
  5. Technique 1: Quantum Information Retrieval
  6. Technique 2: Quantum Modelling and Emulation
  7. Discussions and conclusions
  8. Code availability
  9. Data availability
  10. Acknowledgment
  11. Appendix: Trotterisation

Figures (7)

  • Figure 1: Signal from a selection of channels for pre-, during, and post-epileptic seizure, for a real case study.
  • Figure 2: Sonified signal from the channels of Figure \ref{['iEEG_data_selected_few_no_annotations']}.
  • Figure 3: Spectrogram of the sonified Rolling expectation value of the kurtosis of the MST4 channel encoded using a QPAM approach, simulated.
  • Figure 4: Circuit of a single time evolution step.
  • Figure 5: Reduced, averaged and renormalised dataset for 9 time steps. Top: The outcome of technique 1 ($\langle a^4\rangle$ for MST4). At $t=4$, the amplitude is designed to be exactly 1 (phase transition). Bottom: 16 channels of the ECoG dataset from Figure \ref{['time_series_sonification']}.
  • ...and 2 more figures