Academic Journal
A novel automated autism spectrum disorder detection system
| Τίτλος: | A novel automated autism spectrum disorder detection system |
|---|---|
| Συγγραφείς: | Oh, Shu L., Jahmunah, V., Arunkumar, N., Abdulhay, Enas W., Gururajan, Raj, Adib, Nahrizul, Ciaccio, Edward J., Cheong, Kang H., Acharya, U. R. |
| Έτος έκδοσης: | 2021 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | Autism spectrum disorders, Machine learning--Industrial applications, Autism--Diagnosis, Support vector machines |
| Περιγραφή: | Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| DOI: | 10.7916/yjw0-fa93 |
| Διαθεσιμότητα: | https://doi.org/10.7916/yjw0-fa93 |
| Αριθμός Καταχώρησης: | edsbas.8F233DD0 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.7916/yjw0-fa93# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel automated autism spectrum disorder detection system – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Oh%2C+Shu+L%2E%22">Oh, Shu L.</searchLink><br /><searchLink fieldCode="AR" term="%22Jahmunah%2C+V%2E%22">Jahmunah, V.</searchLink><br /><searchLink fieldCode="AR" term="%22Arunkumar%2C+N%2E%22">Arunkumar, N.</searchLink><br /><searchLink fieldCode="AR" term="%22Abdulhay%2C+Enas+W%2E%22">Abdulhay, Enas W.</searchLink><br /><searchLink fieldCode="AR" term="%22Gururajan%2C+Raj%22">Gururajan, Raj</searchLink><br /><searchLink fieldCode="AR" term="%22Adib%2C+Nahrizul%22">Adib, Nahrizul</searchLink><br /><searchLink fieldCode="AR" term="%22Ciaccio%2C+Edward+J%2E%22">Ciaccio, Edward J.</searchLink><br /><searchLink fieldCode="AR" term="%22Cheong%2C+Kang+H%2E%22">Cheong, Kang H.</searchLink><br /><searchLink fieldCode="AR" term="%22Acharya%2C+U%2E+R%2E%22">Acharya, U. R.</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2021 – Name: Subset Label: Collection Group: HoldingsInfo Data: Columbia University: Academic Commons – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Autism+spectrum+disorders%22">Autism spectrum disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning--Industrial+applications%22">Machine learning--Industrial applications</searchLink><br /><searchLink fieldCode="DE" term="%22Autism--Diagnosis%22">Autism--Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Description Group: Ab Data: Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: DOI Label: DOI Group: ID Data: 10.7916/yjw0-fa93 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.7916/yjw0-fa93 – Name: AN Label: Accession Number Group: ID Data: edsbas.8F233DD0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/yjw0-fa93 Languages: – Text: English Subjects: – SubjectFull: Autism spectrum disorders Type: general – SubjectFull: Machine learning--Industrial applications Type: general – SubjectFull: Autism--Diagnosis Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: A novel automated autism spectrum disorder detection system Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Oh, Shu L. – PersonEntity: Name: NameFull: Jahmunah, V. – PersonEntity: Name: NameFull: Arunkumar, N. – PersonEntity: Name: NameFull: Abdulhay, Enas W. – PersonEntity: Name: NameFull: Gururajan, Raj – PersonEntity: Name: NameFull: Adib, Nahrizul – PersonEntity: Name: NameFull: Ciaccio, Edward J. – PersonEntity: Name: NameFull: Cheong, Kang H. – PersonEntity: Name: NameFull: Acharya, U. R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2021 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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