A novel approach for detection of dyslexia using convolutional neural network with EOG signals.
Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational development; hence, early detection is highly importan...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 11; pp. 3041 - 3056 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159531219&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159531219 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2022 vid: 60 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159531219 158907942 159531219 NLM36063351 10.1007/s11517-022-02656-3 NLM36063351 159531219 ppf: 3041 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A novel approach for detection of dyslexia using convolutional neural network with EOG signals. aug: au: Ileri, Ramis Latifoğlu, Fatma Demirci, Esra affil: Department of Biomedical Engineering, Engineering Faculty, Erciyes University, 39039, Kayseri, Turkey sug: subj: Dyslexia Diagnosis User-Computer Interface Eye Movements Child Electrooculography Methods Electroencephalography Methods Arthritis Impact Measurement Scales Child: 6-12 years ab: Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational development; hence, early detection is highly important. Electrooculogram (EOG) signals are one of the most frequently used physiological signals in human-computer interfaces applications. EOG is a method based on the examination of the electrical potential of eye movements. The advantages of EOG-based systems are non-invasive, affordable, easy to record, and can be processed in real time. In this paper, a novel 1D CNN approach using EOG signals is proposed for the diagnosis of dyslexia. The proposed approach aims to diagnose dyslexia using EOG signals that are recorded simultaneously during reading texts, which are prepared in different typefaces and fonts. EOG signals were recorded from both horizontal and vertical channels, thus comparing the success of vertical and horizontal EOG signals in detecting dyslexia. The proposed approach provided an effective classification without requiring any hand-crafted feature extraction techniques. The proposed method achieved classifier accuracy of 98.70% and 80.94% for horizontal and vertical channel EOG signals, respectively. The results show that the EOG signals-based approach gives successful results for the diagnosis of dyslexia. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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