SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

TL;DR

This paper introduces SSRepL-ADHD, a novel framework using self-supervised representation learning and transfer learning with LSTM-GRU models to improve ADHD detection from EEG signals during visual attention tasks, achieving 81.11% accuracy.

Contribution

It presents a new SSRepL and transfer learning framework combining LSTM and GRU for ADHD detection from EEG data, addressing dataset imbalance and feature selection challenges.

Findings

SSRepL-ADHD achieved 81.11% accuracy.

The framework effectively captures temporal dependencies in EEG data.

Models outperform traditional methods on ADHD detection metrics.

Abstract

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performance on also downstream different types of Neurodevelopmental disorder (NDD) detection. In this paper, a novel SSRepL and Transfer Learning (TL)-based framework that incorporates a Long Short-Term Memory (LSTM) and a Gated Recurrent Units (GRU) model is proposed to detect children with potential symptoms of ADHD. This model uses Electroencephalogram (EEG) signals extracted during visual attention tasks to accurately detect ADHD by preprocessing EEG signal quality through normalization, filtering, and data balancing. For the experimental analysis, we use three different models: 1) SSRepL and TL-based LSTM-GRU model named as SSRepL-ADHD, which integrates LSTM and GRU layers to capture temporal dependencies in the data, 2) lightweight SSRepL-based DNN model (LSSRepL-DNN), and 3) Random Forest (RF). In the study, these models are thoroughly evaluated using well-known performance metrics (i.e., accuracy, precision, recall, and F1-score). The results show that the proposed SSRepL-ADHD model achieves the maximum accuracy of 81.11% while admitting the difficulties associated with dataset imbalance and feature selection.