Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features

• Autorzy: Altınkaynak Miray , Dolu Nazan , Güven Aysegül , Pektas Ferhat , Özmen Sevgi , Demirci Esra , Izzetoglu Meltem

Identyfikatory

DOI

10.1016/j.bbe.2020.04.006

• Języki publikacji: EN

Abstrakty

EN

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 observed in ADHD patients relative to controls. In fractal dimension calculation relative to the control group, the ADHD group demonstrated reduced complexity. In addition, certain wavelet coefficients provided significantly different values in both groups. Combining these extracted features, our results indicated that Multilayer Perceptron method provided the best classification with an accuracy rate of 91.3% and a high level of reliability of concurrence (Kappa = 0.82). The results showed that combining time and frequency domain features can be a useful and discriminative for diagnostic purposes in ADHD. The study presents a supporting diagnostic tool that uses EEG signal processing and machine learning algorithms. The findings would be helpful in the objective diagnosis of ADHD.

Słowa kluczowe

EN

Attention Deficit Hyperactivity Disorder; machine learnig; auditory evoked potentials; fractal dimension; discrete wavelet transform

PL

zespół nadpobudliwości psychoruchowej; uczenie maszynowe; słuchowe potencjały wywołane; wymiar fraktalny; transformacja falkowa dyskretna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 927--937
• Opis fizyczny: Bibliogr. 54 poz., rys., tab.

Twórcy

autor

Altınkaynak Miray

• Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, 38039 Kayseri, Turkey

autor

Dolu Nazan

• Department of Physiology, Medical Faculty, Baskent University, Turkey

autor

Güven Aysegül

• Department of Biomedical Engineering, Engineering Faculty, Erciyes University, Turkey

autor

Pektas Ferhat

• Department of Physiology, Medical Faculty, Kırsehir Ahi Evran University, Turkey

autor

Özmen Sevgi

• Department of Child Psychiatry, Medical Faculty, Erciyes University, Turkey

autor

Demirci Esra

• Department of Child Psychiatry, Medical Faculty, Erciyes University, Turkey

autor

Izzetoglu Meltem

• Electrical and Computer Engineering Department, Villanova University, USA

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).