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{{{There has been significant expansion of wearable technologies and devices to either directly or indirectly identifying seizure activity. Current devices typically monitor a number of parameters including electroencephalographic (EEG), cardiac, and respiratory patterns and can detect movement, changes in skin conductance, and muscle activity. This is a novel study investigating sound detection as an audio-based detection of major seizures- with mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00), 4 seizures (3%) were missed because of lack of sound and 10 (9%) because of sounds below the system threshold. It’s possible that audio-based detection may be useful in a multimodal device for seizure detection. While Generalized tonic clonic seizures (GTC’s) are reliably identified by most unimodal and multimodal devices, detection of seizures with little or no movement remains a challenge. | {{{There has been significant expansion of wearable technologies and devices to either directly or indirectly identifying seizure activity. Current devices typically monitor a number of parameters including electroencephalographic (EEG), cardiac, and respiratory patterns and can detect movement, changes in skin conductance, and muscle activity. This is a novel study investigating sound detection as an audio-based detection of major seizures- with mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00), 4 seizures (3%) were missed because of lack of sound and 10 (9%) because of sounds below the system threshold. It’s possible that audio-based detection may be useful in a multimodal device for seizure detection. While Generalized tonic clonic seizures (GTC’s) are reliably identified by most unimodal and multimodal devices, detection of seizures with little or no movement remains a challenge. See article The role of devices in managing risk. Rugg-Gun F. Epilepsy Behav. 2020 Feb;103(Pt B):106456. doi: 10.1016/j.yebeh.2019.106456. Epub 2019 Aug 17. | ||
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Revision as of 04:34, 23 February 2020
Namespace: Template
Name: Reference
Purpose: Use this template for standard reference pages on this site.
Parameters:
- reference - reference for the article (e.g., Annegers JF (1997) United States perspective on definitions and classifications. Epilepsia. 1997 Nov;38(11 Suppl):S9-12.) - url - URL for the full article (e.g., https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1528-1157.1997.tb06137.x) - abstract - the article's abstract - keywords - a comma-delimited list of key words (e.g., classification, postmortem, definitions) - context - context of the article - comments - comments about the article
Requires:
/index.php/MediaWiki:Network-graph.js /index.php/MediaWiki:Common.js
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[{{{url}}} Link to Article]
Abstract: {{{abstract}}}
Keywords: {{{keywords}}}
Context
{{{There has been significant expansion of wearable technologies and devices to either directly or indirectly identifying seizure activity. Current devices typically monitor a number of parameters including electroencephalographic (EEG), cardiac, and respiratory patterns and can detect movement, changes in skin conductance, and muscle activity. This is a novel study investigating sound detection as an audio-based detection of major seizures- with mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00), 4 seizures (3%) were missed because of lack of sound and 10 (9%) because of sounds below the system threshold. It’s possible that audio-based detection may be useful in a multimodal device for seizure detection. While Generalized tonic clonic seizures (GTC’s) are reliably identified by most unimodal and multimodal devices, detection of seizures with little or no movement remains a challenge. See article The role of devices in managing risk. Rugg-Gun F. Epilepsy Behav. 2020 Feb;103(Pt B):106456. doi: 10.1016/j.yebeh.2019.106456. Epub 2019 Aug 17. }}}
Comments
{{{comments}}}