Title: Adaptive neuro-fuzzy-based attention deficit/hyperactivity disorder diagnostic system

Authors: Anoop Kumar Singh; Deepti Kakkar; Tanu Wadhera; Rajneesh Rani

Addresses: Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India ' Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India ' Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India ' Department of Computer Science Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India

Abstract: The main purpose of this research paper is to develop a simple automated system for the accurate diagnosis of attention deficit/hyperactivity disorder (ADHD) using the adaptive neuro-fuzzy inference system (ANFIS). The designed diagnostic system has two stages-primary and secondary. In the primary stage, a hierarchical fuzzy-based short version of the gold diagnostic tool Connor's scale has been implemented to evaluate the behavioural aspects in a fast and simple manner. The secondary stage targets the two main abilities of brain functionality-attention and perception. The determining traits were extracted from ERP components, especially the P300 wave, using peak amplitude and average latency rate. The proposed secondary diagnostic stage is based on Takagi-Sugeno fuzzy inference system and it integrates the features of both artificial neural network and fuzzy logic into a single framework. The system accuracy is 99.3% in classification, i.e., ADHD vs. Normal and 88.78% in severity level (normal/low, medium and high) of ADHD. Thus, the proposed model provides an adaptive and better alternative to ADHD diagnosis.

Keywords: attention deficit/hyperactivity disorder; ADHD; artificial neural network; fuzzy logic; backpropagation; ANFIS; neuro-fuzzy inference system; event-related potential; FIS; standalone fuzzy inference system.

DOI: 10.1504/IJMEI.2021.118763

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.6, pp.487 - 496

Received: 17 Sep 2019
Accepted: 25 Jan 2020
Published online: 05 Nov 2021 *

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