No Detail Found!

Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using AutoEncoder Based Hidden Markov Model

Author(s)
Mahesh T R, Department of Computer Science and Engineering, Jain (Deemed-to-be University), Bangalore, India
Tanmay Goswami Department of Ayurveda, Sanskriti University, Mathura, Uttar Pradesh, India
Srinivasan Sriramulu School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
Neeraj Sharma Department of Computer Science Engineering, Chandigarh Engineering College, Jhanjeri, India
Alka Kumari Department of Computer Science, ARKA JAIN University, Jamshedpur, Jharkhand, India
Ganesh Khekare Department of Computer Science and Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India.
Abstract
Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as a cognitive complaint. Behavioural analysis of ADHD consumes more time. This is a multi-informant complex procedure due to the overlaps in symptomatology which is the cause for delay in diagnosis and treatment. Dur to these behavioural consequences and various causes, no single test is utilized till now for diagnosing this disorder. Hence, a diagnosing model of ADHD based on Continuous Ability Assessment Test (CAAT) can enhance and balance behavioural assessment. The objective behind this study is to use a deep learning based model with CAAT for predicting ADHD. The proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features of brain structures, and a novel Pearson Correlation Coefficient (PCC) is employed for normalizing these features in order to minimize batch effects over populations and datasets. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like CogniLearn and 3-D Convolutional Neural Networks (3DCNN). It is found that the proposed AE-HMM method achieves 93.68% of accuracy, 90.66% of sensitivity, 87.72% of specificity, 87.78% of F1-score and 74.22% of kappa score.
Publication Details
Page(s) 53-65
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 2, Year: 2022

Request the full text from PASTIC

Keywords
cognitive, Hidden Markov Model (HMM), Feature extraction., Neurogenerative disease, ability assessment
More from International Journal of Communication Networks and Information Security
0

Citations

0

Downloads

52

Views

Copyright ©  PASTIC National Center, Islamabad www.pastic.gov.pk