Key result
EEG signal analysis using permutation entropy and an artificial neural network classifier achieved a classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82% for detecting ADHD.
Why the study?
Current ADHD assessment methods like clinical evaluations and neuropsychological assessments are time-consuming and carry uncertainty, prompting investigation into an efficient computer-aided detection method using EEG signals.
Does an artificial neural network classifier using nonlinear entropy estimators from EEG signals accurately detect ADHD compared to normal subjects?
Cross-Sectional
Does an artificial neural network classifier using nonlinear entropy estimators from EEG signals accurately detect ADHD compared to normal subjects?
Answers are grounded in this paper and cite their sources.
Permutation entropy combined with an artificial neural network classifier can detect ADHD from EEG signals with high accuracy.
Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.
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Joy et al. (2021) conducted a cross-sectional in Attention deficit hyperactivity disorder (ADHD). EEG signal analysis using nonlinear entropy estimators and an artificial neural network classifier vs. Control group was evaluated on Classification accuracy for detecting ADHD using permutation entropy. EEG signal analysis using permutation entropy and an artificial neural network classifier achieved a classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82% for detecting ADHD.
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