Detecting Attention Deficit Hyperactivity Disorder (ADHD) Inflicted Humans using Long Short-Term Memory (LSTM)

Abstract

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a common psychiatric disorder characterised by persistent patterns of inattention, hyperactivity, and impulsivity in children. The risk factor is that these children are frequently entangled in learning difficulties, which lead to frustration when they reach adulthood. This study uses functional Magnetic Resonance Imaging data for the resting-state brain to present an effective approach for ADHD identification at an early stage. The proposed method is based on seed correlation, which calculates the functional connectivity between seeds and all other voxels in the brain.This paper gives a walk through of the steps for using machine learning to analyse rs-fMRI data and, more specifically, to distinguish Attention Deficit Hyperactivity Disorder (ADHD) from healthy controls. I discuss (1) feature extraction with masks, (2) the advantages and disadvantages of long short term memory networks (LSTM) for classifying fMRI data, and (3) hypothesis testing and its application in model evaluation. Keywords: ADHD, f-MRI, Impulsive behaviour, Anxiety, LSTM