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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

21
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
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Automated ADHD detection using dual-modal sensory data and machine learning.

Yanqing Ji1, Janet Zhang-Lea2, John Tran3

  • 1Dept of Electrical & Computer Engineering, Gonzaga University, Spokane, USA.

Medical Engineering & Physics
|April 30, 2025
PubMed
Summary

Objective ADHD identification is advanced using dual-modal sensory data and machine learning. Combining activity and heart rate variability data significantly improved diagnostic accuracy, with SVM showing the best performance.

Keywords:
ADHD detectionAttention-Deficit/Hyperactivity Disorder (ADHD)HRV and activity dataMachine learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis traditionally relies on subjective clinical assessments.
  • Objective diagnostic methods are needed to improve accuracy and accessibility for ADHD.
  • Dual-modal sensory data offers potential for more comprehensive patient profiling.

Purpose of the Study:

  • To investigate the efficacy of machine learning algorithms in objectively identifying ADHD using dual-modal sensory data.
  • To compare the diagnostic performance of activity and heart rate variability (HRV) data, individually and combined.
  • To determine the optimal machine learning model for ADHD detection with integrated sensory inputs.

Main Methods:

  • Collected activity and heart rate variability (HRV) data from 103 participants.
  • Evaluated six machine learning algorithms: Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), LightGBM (LGBM), Neural Network (NN), and Support Vector Machine (SVM).
  • Assessed model performance using F1-score and Matthews Correlation Coefficient (MCC).

Main Results:

  • Individual analysis of activity and HRV data yielded similar performance metrics.
  • Combining activity and HRV data significantly enhanced diagnostic performance.
  • The Support Vector Machine (SVM) model achieved the highest F1-Score of 0.87 and MCC of 0.77 with combined data.
  • Combined data improved F1-score by 12% over activity data alone and 23% over HRV data alone.

Conclusions:

  • Dual-modal sensory data, particularly when combined, offers a promising avenue for objective ADHD identification.
  • Machine learning, specifically SVM, can effectively leverage integrated activity and HRV data for improved ADHD detection.
  • This interdisciplinary approach highlights the potential for advanced technological solutions in neurodevelopmental disorder diagnostics.