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MindReader :
MindReader : unsupervised electroencephalographic reader
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- Rivas-Carrillo, Salvador Daniel (author)
- Uppsala universitet,Institutionen för medicinsk biokemi och mikrobiologi
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Akkuratov, Evgeny E. (author)
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Valdez Ruvalcaba, Hector (author)
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Vargas-Sanchez, Angel (author)
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San-Juan, Daniel (author)
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- Grabherr, Manfred G. (author)
- Uppsala universitet,Institutionen för medicinsk biokemi och mikrobiologi
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show less...
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(creator_code:org_t)
- 2023
- English.
- Related links:
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https://urn.kb.se/re...
Abstract
Subject headings
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- Background: Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, including epilepsy. Manual analysis requires highly specialized and heavily trained personnel. Moreover, the rate of capturing abnormal events makes interpretation time-consuming, resource-hungry, and, overall, an expensive process.Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data, and optimizing the allocation of human resources.Findings: We present MindReader, an unsupervised method for EEG signals. First, MindReader processes the signal through an autoencoder in order to detect EEG abnormalities. Next, patterns are hypothesized by a Hidden Markov Model. Our algorithm automatically generates labels for non-pathological phases, thus reducing the search space for trained personnel.Conclusions: MindReader is effective in detecting EEG abnormalities in focal and generalized epilepsy.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Bioinformatics
- Bioinformatik
Publication and Content Type
- vet (subject category)
- ovr (subject category)
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