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MindReader :
MindReader : Unsupervised Classification of Electroencephalographic Data
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- Rivas-Carrillo, Salvador Daniel (author)
- Uppsala universitet,Institutionen för medicinsk biokemi och mikrobiologi,Beräkningsbiologi och bioinformatik
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- Akkuratov, Evgeny E. (author)
- KTH,Biofysik,Science for Life Laboratory, SciLifeLab,Science for Life Laboratory, Department of Applied Physics, Royal Institute of Technology, 11428 Stockholm, Sweden
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- Ruvalcaba, Hector Valdez (author)
- Epilepsy Clin, Inst Nacl Neurol & Neurocirugia, Mexico City 14269, DF, Mexico.,Epilepsy Clinic, Instituto Nacional de Neurologia y Neurocirugía, Mexico City 14269, Mexico
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- Vargas-Sanchez, Angel (author)
- Independent Researcher, Guadalajara 44670, Mexico
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- Komorowski, Jan (author)
- Uppsala universitet,Beräkningsbiologi och bioinformatik,Washington National Primate Research Center, Seattle, WA 98121, USA; The Institute of Computer Science, Polish Academy of Sciences, 01-248 Warsaw, Poland
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- San-Juan, Daniel (author)
- Epilepsy Clin, Inst Nacl Neurol & Neurocirugia, Mexico City 14269, DF, Mexico.,Epilepsy Clinic, Instituto Nacional de Neurologia y Neurocirugía, Mexico City 14269, Mexico
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- Grabherr, Manfred G. (author)
- Uppsala universitet,Institutionen för medicinsk biokemi och mikrobiologi
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(creator_code:org_t)
- 2023-03-09
- 2023
- English.
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In: Sensors. - : MDPI AG. - 1424-8220. ; 23:6, s. 2971-
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Abstract
Subject headings
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- Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure 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 towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- electroencephalography
- machine learning
- precision medicine
- unsupervised learning
- Artificiell intelligens
Publication and Content Type
- ref (subject category)
- art (subject category)
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