ANALYSIS OF NEURODEVELOPMENTAL DISORDER IN SPECIAL CHILD FROM EEG USING MACHINE LEARNING
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Apr 10, 2021
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Abstract
Angelman syndrome (AS) is a neurodevelopmental disease characterised by learning disability, speechand motor impairments, epilepsy, erratic sleeping habits, and a constitution that overlaps with the syndrome.Electroencephalogram patterns and high-amplitude intermittent delta waves are popular in people with AS. We are looking for a way to quantitatively investigate electroencephalogram architecture in AS. Hybridization classifier was implemented, as well as the suggestion of patients' care as a gym and stress relief level. Children with AS (ages 4–11) and age-matched neurotypical controls had their wake and sleep EEGs analysed retrospectively. To assess long-range and short-range purposeful properties, we measure coherence over many frequencies during wake and sleep.We use both automated and manual methods to quantify sleep spindles victimisation. We incorporate a real-time health monitoring system in this paper
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
[1]
Swati G. Gawhale, Dr. Dhananjay E. Upasani, and Dr. Anupama Deshpande, “ANALYSIS OF NEURODEVELOPMENTAL DISORDER IN SPECIAL CHILD FROM EEG USING MACHINE LEARNING”, IEJRD - International Multidisciplinary Journal, vol. 6, no. ICMRD21, p. 7, Apr. 2021.
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