Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder

• Published in: Neural computing & applications 2020-06, Vol.32 (12), p.8367-8380
• Main Authors: Güven, Ayşegül, Altınkaynak, Miray, Dolu, Nazan, İzzetoğlu, Meltem, Pektaş, Ferhat, Özmen, Sevgi, Demirci, Esra, Batbat, Turgay
• Format: Article
• Language: English
• Subjects: Accuracy; Amplitudes; Artificial Intelligence; Auditory evoked potentials; Brain; Classification; Complexity; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Diagnosis; Electroencephalography; Fractal geometry; Hemodynamic responses; Image Processing and Computer Vision; Infrared spectra; Infrared spectroscopy; Machine learning; Medical imaging; Multilayers; Near infrared radiation; Neural networks; Nonlinear dynamics; Original Article; Probability and Statistics in Computer Science; Spectrum analysis; Support vector machines
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04294-7

Recently multimodal neuroimaging which combines signals from different brain modalities has started to be considered as a potential to improve the accuracy of diagnosis. The current study aimed to explore a new method for discriminating attention-deficit hyperactivity disorder (ADHD) patients and control group by means of simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Twenty-three pre-medicated combined type ADHD children and 21 healthy children were included in the study. Nonlinear brain dynamics of subjects were obtained from EEG signal using Higuchi fractal dimensions and Lempel–Ziv complexity, latency and amplitude values of P3 wave obtained from auditory evoked potentials and frontal cortex hemodynamic responses calculated from fNIRS. Lower complexity values, prolonged P3 latency and reduced P3 amplitude values were found in ADHD children. fNIRS indicated that the control subjects exhibited higher right prefrontal activation than ADHD children. Features are analyzed, looking for the best classification accuracy and finally machine learning techniques, namely Support Vector Machines, Naïve Bayes and Multilayer Perception Neural Network, are introduced for EEG signals alone and for combination of fNIRS and EEG signals. Naive Bayes provided the best classification with an accuracy rate of 79.54% and 93.18%, using EEG and EEG-fNIRS systems, respectively. Our findings demonstrate that utilization of information by combining features obtained from fNIRS and EEG improves the classification accuracy. As a conclusion, our method has indicated that EEG-fNIRS multimodal neuroimaging is a promising method for ADHD objective diagnosis.