背景:注意缺陷/多动障碍(ADHD)是一种多方面的神经发育性精神疾病,通常在童年时期出现,但通常持续到成年期,显著影响个人功能,关系,生产力,和整体生活质量。然而,当前的诊断过程显示出可能显著影响其整体有效性的局限性.值得注意的是,它的面对面和耗时的性质,再加上对历史信息主观回忆和临床医生主观性的依赖,成为关键挑战。为了解决这些限制,客观措施,如神经心理学评估,自主神经系统功能的成像技术和生理监测,已经被探索过了。
方法:本研究的主要目的是调查生理数据是否(即,皮肤电活动,心率变异性,和皮肤温度)可以作为ADHD的有意义的指标,评估其在区分成人ADHD患者中的实用性。这个观测,病例对照研究包括总共76名成年参与者(32名ADHD患者和44名健康对照),他们接受了一系列Stroop测试,而他们的生理数据是使用多传感器可穿戴设备被动收集的。单变量特征分析用于识别触发显著信号响应的测试,而信息k最近邻(KNN)算法用于过滤信息较少的数据点。最后,包含各种分类算法的机器学习决策管道,包括Logistic回归,KNN,随机森林,和支持向量机(SVM),用于ADHD患者检测。
结果:结果表明,基于SVM的模型具有最佳性能,达到81.6%的精度,保持实验组和对照组之间的平衡,敏感性和特异性分别为81.4%和81.9%,分别。此外,整合所有生理信号的数据产生了最好的结果,表明每种模式都能捕捉到多动症的独特方面。
结论:本研究强调了生理信号作为成人多动症有价值的诊断指标的潜力。第一次,据我们所知,我们的研究结果表明,通过可穿戴设备收集的多模式生理数据可以补充传统的诊断方法.需要进一步的研究来探索在ADHD诊断和管理中利用生理标志物的临床应用和长期影响。
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals\' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored.
METHODS: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection.
RESULTS: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD.
CONCLUSIONS: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.