Deep Learning-Based Modified Bidirectional LSTM Network for Classification of ADHD Disorder

• Published in: Arabian journal for science and engineering (2011) Vol. 49; no. 3; pp. 3009 - 3026
• Main Authors: Saurabh, Sudhanshu, Gupta, P. K.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024; Springer Nature B.V
• Subjects: Attention deficit hyperactivity disorder; Classification; Deep learning; Engineering; Humanities and Social Sciences; Medical imaging; multidisciplinary; Neurological diseases; Research Article-Computer Engineering and Computer Science; Science; ADHD; Resting State Network; Tensor; Bidirectional LSTM; Functional Connectivity; Deep Learning
• DOI: 10.1007/s13369-023-07786-w
• ISSN: 2193-567X; 1319-8025

Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that affects an individual’s behavior. The rising cases of ADHD among children and adolescents worldwide have raised the concern and require techniques for its early diagnosis and identification. The symptoms of ADHD are characterized by patterns of hyperactivity, inattention, and impulsivity. Recent advances in neuroimaging have allowed researchers to obtain the functional and structural patterns of the brain affected by ADHD. This work considers the resting state functional magnetic imaging (rs-fMRI) data and analyzes the functional connectivity of 40 subjects (20 ADHD and 20 healthy controls) through voxel size blood-oxygen-level-dependent (BOLD) signal. These BOLD signals are functionally relevant to the corresponding resting state networks (RSN). In this paper, we have proposed a modified deep learning-based bidirectional long short-term memory (BLSTM) model that automates the classification of ADHD through the identified voxels within the active region of the RSN. Initially, we have visualized the 28 active regions of RSN and time series of behavioral data of 40 subjects with 176 time stamps. Then, the proposed modified BLSTM has been trained by using the feature vector ( 40 × 261 × 28 ) for each subject and Adam hyper-parameter for optimization. The experimental results represent that the proposed model outperforms the many other models by achieving the classification accuracy of 87.50 % . We have also provided a detailed comparative analysis of the proposed model with the different existing state-of-the-art approaches.