LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals

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dc.authoridDeniz, Erkan/0000-0002-9048-6547
dc.authoridATİLA, Orhan/0000-0001-7211-913X
dc.authoridChakraborty, Subrata/0000-0002-0102-5424
dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.authoridBarua, Prabal Datta/0000-0001-5117-8333
dc.authoridAcharya, U Rajendra/0000-0003-2689-8552
dc.authorwosidDeniz, Erkan/V-5545-2018
dc.authorwosidATİLA, Orhan/AAU-6277-2020
dc.authorwosidChakraborty, Subrata/IXX-0792-2023
dc.authorwosidSengur, Abdulkadir/V-7812-2018
dc.contributor.authorAtila, Orhan
dc.contributor.authorDeniz, Erkan
dc.contributor.authorAri, Ali
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorChakraborty, Subrata
dc.contributor.authorBarua, Prabal Datta
dc.contributor.authorAcharya, U. Rajendra
dc.date.accessioned2024-08-04T20:54:38Z
dc.date.available2024-08-04T20:54:38Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAnxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n x n sliding window is applied to this image for patch extraction. An n x n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.en_US
dc.identifier.doi10.3390/s23167032
dc.identifier.issn1424-8220
dc.identifier.issue16en_US
dc.identifier.pmid37631569en_US
dc.identifier.scopus2-s2.0-85168740476en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/s23167032
dc.identifier.urihttps://hdl.handle.net/11616/101537
dc.identifier.volume23en_US
dc.identifier.wosWOS:001062677600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectADHD detectionen_US
dc.subjectEEG signalsen_US
dc.subjectUlam's spiralen_US
dc.subjectSophie Germain's primesen_US
dc.subjectSVMen_US
dc.titleLSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signalsen_US
dc.typeArticleen_US

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