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相关概念视频

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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使用双模态感官数据和机器学习的自动化ADHD检测.

Yanqing Ji1, Janet Zhang-Lea2, John Tran3

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

Medical engineering & physics
|April 30, 2025
PubMed
概括

客观的ADHD识别是使用双模态感官数据和机器学习进行的. 结合活动和心率变化数据,显著提高了诊断准确度,SVM表现最好.

关键词:
发现ADHD的检测注意缺陷/多动障碍 (ADHD) 是一种注意缺陷/多动障碍.人力资源和活动数据.机器学习 机器学习

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Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 生物医学工程 生物医学工程

背景情况:

  • 注意缺陷/多动症 (ADHD) 诊断传统上依赖于主观的临床评估.
  • 需要客观的诊断方法来提高ADHD的准确性和可访问性.
  • 双模传感数据为更全面的患者分析提供了潜力.

研究的目的:

  • 通过使用双模态感官数据,研究机器学习算法在客观识别ADHD方面的有效性.
  • 为了比较活动和心率变化 (HRV) 数据的诊断性能,单独和组合.
  • 通过集成的感官输入来确定ADHD检测的最佳机器学习模型.

主要方法:

  • 收集了来自103名参与者的活动和心率变化 (HRV) 数据.
  • 评估了六种机器学习算法:逻辑回归 (LR),随机森林 (RF),XGBoost (XGB),LightGBM (LGBM),神经网络 (NN) 和支持矢量机器 (SVM).
  • 使用F1得分和马修斯相关系数 (MCC) 评估模型性能.

主要成果:

  • 对活动和HRV数据的个人分析产生了类似的绩效指标.
  • 结合活动和HRV数据显著提高了诊断性能.
  • 支持矢量机 (SVM) 模型实现了最高的F1-Score0.87和MCC0.77的组合数据.
  • 综合数据比单独的活动数据提高了F1分数12%,比单独的HRV数据提高了23%.

结论:

  • 双模传感数据,特别是当结合时,为客观ADHD识别提供了一个有希望的途径.
  • 机器学习,特别是SVM,可以有效地利用综合活动和HRV数据来改善ADHD检测.
  • 这种跨学科的方法凸显了神经发育障碍诊断中先进技术解决方案的潜力.