Sounds of seizures. Academic Article uri icon

Overview

abstract

  • PURPOSE: A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings. METHODS: We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated. RESULTS: The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97). CONCLUSION: This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.

authors

  • Shum, Jennifer
  • Fogarty, Adam
  • Dugan, Patricia
  • Holmes, Manisha G
  • Leeman-Markowski, Beth A
  • Liu, Anli A
  • Fisher, Robert S
  • Friedman, Daniel

publication date

  • March 18, 2020

Research

keywords

  • Automatism
  • Epilepsy
  • Respiratory Sounds
  • Seizures
  • Sound

Identity

PubMed Central ID

  • PMC7269794

Scopus Document Identifier

  • 85082771037

Digital Object Identifier (DOI)

  • 10.1111/epi.13899

PubMed ID

  • 32276233

Additional Document Info

volume

  • 78