Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals

Basit Öğe Kaydı
dc.authoridhttps://orcid.org/ 0000-0002-2917-3736en_US
dc.contributor.authorArı, Berna
dc.contributor.authorSobahi, Nebras
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorAcharya, U.Rajendra
dc.date.accessioned2022-03-22T13:14:44Z
dc.date.available2022-03-22T13:14:44Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAutism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application.en_US
dc.identifier.citationAri, B., Sobahi, N., Alçin, Ö. F., Sengur, A., & Acharya, U. R. (2022). Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals. Computers in Biology and Medicine, 143, 105311.en_US
dc.identifier.doi10.1016/j.compbiomed.2022.105311
dc.identifier.endpage10en_US
dc.identifier.issue105311en_US
dc.identifier.pmid35158117
dc.identifier.scopus2-s2.0-85124383156en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2022.105311
dc.identifier.urihttps://hdl.handle.net/20.500.12899/781
dc.identifier.volume143en_US
dc.identifier.wosWOS:000790189400006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherElsevier B.V. Allen_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutism spectrum disorderen_US
dc.subjectEEG signalsen_US
dc.subjectDouglas-Peucker algorithmen_US
dc.subjectDeep learningen_US
dc.titleAccurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signalsen_US
dc.typeArticleen_US

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