Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features
Yazarlar (7)
Miray Altınkaynak
Erciyes Üniversitesi, Türkiye
Erciyes Üniversitesi, Türkiye
Prof. Dr. Nazan Dolu Başkent Üniversitesi, Türkiye
Ayşegül Güven Erciyes Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ferhat PEKTAŞ Kırşehir Ahi Evran Üniversitesi, Türkiye
Doç. Dr. Sevgi Özmen Erciyes Üniversitesi, Türkiye
Prof. Dr. Esra Demirci Erciyes Üniversitesi, Türkiye
Meltem İzzetoğlu Villanova University College Of Engineering, Amerika Birleşik Devletleri
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biocybernetics and Biomedical Engineering | ||
| Dergi ISSN | 0208-5216 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 07-2020 |
| Cilt / Sayı / Sayfa | 40 / 3 / 927–937 | DOI | 10.1016/j.bbe.2020.04.006 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0208521620300565 | ||
| Özet |
| The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event Related Potentials (ERP) obtained from Electroencephalography (EEG) signals while participants performed an auditory oddball task. The study included 23 unmedicated ADHD patients and 23 healthy controls. The EEG signal was analyzed in time domain by nonlinear brain dynamics and morphological features, and in time-frequency domain with wavelet coefficients. Selected features were applied to various machine learning techniques including; Multilayer Perceptron, Naïve Bayes, Support Vector Machines, k-nearest neighbor, Adaptive Boosting, Logistic Regression and Random Forest to classify ADHD patients and healthy controls. Longer P300 latencies and smaller P300 amplitudes were … |
| Anahtar Kelimeler |
| Attention Deficit Hyperactivity Disorder | Auditory evoked potentials | Classification | Discrete Wavelet Transform | Fractal dimension | Machine learning |
Science Direct
Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 69 |
| Google Scholar | 89 |