{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:21:35Z","timestamp":1771885295652,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T00:00:00Z","timestamp":1706313600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epileptic Disorders"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop and validate a method for long\u2010term (24\u2010h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst\u00ae 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60\u2010fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Eleven waEEG recordings from seven CAE patients with an average age of 8.18\u2009\u00b1\u20091.60\u2009years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (<jats:italic>\u03c1<\/jats:italic>\u2009&gt;\u2009.89, <jats:italic>p<\/jats:italic>\u2009&lt;\u2009.001) and between observers (<jats:italic>\u03c1<\/jats:italic>\u2009&gt;\u2009.96, <jats:italic>p<\/jats:italic>\u2009&lt;\u2009.001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post\u2010processed automatic detection by an experienced clinical neurophysiologist, but in a less time\u2010consuming procedure and without the need for specialized resources. Sonification of long\u2010term EEG recordings in CAE provides a user\u2010friendly and cost\u2010effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.<\/jats:p><\/jats:sec>","DOI":"10.1002\/epd2.20194","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T14:44:29Z","timestamp":1706366669000},"page":"188-198","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The sound of silence: Quantification of typical absence seizures by sonifying <scp>EEG<\/scp> signals from a custom\u2010built wearable device"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0189-7908","authenticated-orcid":false,"given":"Daniel Filipe","family":"Borges","sequence":"first","affiliation":[{"name":"Department of Neurophysiology, School of Health (ESS) Polytechnic University of Porto  Porto 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