EEG based ASD diagnosis for children using auto-regressive features and FFNN
By: Laxmi Raja
.
Contributor(s): Mohana Priya.
Publisher: Haryana International Science Press 2021Edition: Vol.13(1), Jan-June.Description: 1-5p.Subject(s): Computer EngineeringOnline resources: Click here
In:
International journal of artificial intelligence and computational researchSummary: Autism spectrum disorder is the common term given to a group of complex disorders of brain and neurodevelopment. Social interactive defects, Verbal and non-verbal communication disorders and repetitive behaviours are common characteristics of autism. Electroencephalography is a medical imaging technique that has been known to be a precisely suitable tool to study the signals generated by brain signal and its activities. In this study, variations in brain EEG signals are identified based on Auto-Regressive features to find difference between normal and autistic children. Maximum classification accuracy of 92.69% is achieved using EFNN.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1080 |
Autism spectrum disorder is the common term given to a group of complex disorders of brain and neurodevelopment. Social interactive defects, Verbal and non-verbal communication disorders and repetitive behaviours are common characteristics of autism. Electroencephalography is a medical imaging technique that has been known to be a precisely suitable tool to study the signals generated by brain signal and its activities. In this study, variations in brain EEG signals are identified based on Auto-Regressive features to find difference between normal and autistic children. Maximum classification accuracy of 92.69% is achieved using EFNN.
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