OBJECTIVES: Children with Bipolar Mood Disorder (BMD) and those with Attention Deficit Hyperactivity Disorder (ADHD) share many clinical signs and symptoms; therefore, achieving an accurate diagnosis is still a challenge, especially in the first interview session. The main focus of this paper is to quantitatively classify the ADHD and BMD patients using their Visual Evoke Potential (VEP) features elicited from their Electroencephalogram (EEG) signals. METHODS AND MATERIALS: In this study, 36 subjects were participated including 12 healthy ones, 12 patients with ADHD and 12 ones with BMD. The age of ADHD patients was 16.92±6.29 and for the BMD ones was 17.85±3.68. Their scalp EEG signals in the presence of visual stimulus were recorded using 22 silver electrodes located according to the 10-20 international recording protocol. To extract their VEP, first a preprocessing step was executed to remove the power line and movement artifacts. Afterward, the wavelet denoising and synchronous averaging were applied to the preprocessed trials in order to elicit the P100 component. To obtain interpretable features from the evoked patterns, amplitude and latency were extracted and applied to the 1-Nearest Neighbor (1NN) classifier due to the locally scattered distribution of the VEP features. RESULTS: The evaluation was performed according to leave-one(subject)-out method and the experimental results were led to 92.85% classification accuracy which is a fairly promising achievement to distinguish the BMD, ADHD, and healthy subjects from each other. CONCLUSION: From the physiological point of view, this result point out to the existence of significant difference in the neural activities of their visual system in the ADHD, BMD, and healthy subjects in response to a periodic optical stimulus.
OBJECTIVES:Children with Bipolar Mood Disorder (BMD) and those with Attention Deficit Hyperactivity Disorder (ADHD) share many clinical signs and symptoms; therefore, achieving an accurate diagnosis is still a challenge, especially in the first interview session. The main focus of this paper is to quantitatively classify the ADHD and BMDpatients using their Visual Evoke Potential (VEP) features elicited from their Electroencephalogram (EEG) signals. METHODS AND MATERIALS: In this study, 36 subjects were participated including 12 healthy ones, 12 patients with ADHD and 12 ones with BMD. The age of ADHDpatients was 16.92±6.29 and for the BMD ones was 17.85±3.68. Their scalp EEG signals in the presence of visual stimulus were recorded using 22 silver electrodes located according to the 10-20 international recording protocol. To extract their VEP, first a preprocessing step was executed to remove the power line and movement artifacts. Afterward, the wavelet denoising and synchronous averaging were applied to the preprocessed trials in order to elicit the P100 component. To obtain interpretable features from the evoked patterns, amplitude and latency were extracted and applied to the 1-Nearest Neighbor (1NN) classifier due to the locally scattered distribution of the VEP features. RESULTS: The evaluation was performed according to leave-one(subject)-out method and the experimental results were led to 92.85% classification accuracy which is a fairly promising achievement to distinguish the BMD, ADHD, and healthy subjects from each other. CONCLUSION: From the physiological point of view, this result point out to the existence of significant difference in the neural activities of their visual system in the ADHD, BMD, and healthy subjects in response to a periodic optical stimulus.
Authors: Joseph J Shaffer; Casey P Johnson; Jess G Fiedorowicz; Gary E Christensen; John A Wemmie; Vincent A Magnotta Journal: Brain Imaging Behav Date: 2018-06 Impact factor: 3.978