Bouchouras Georgios, Sofianidis Georgios, Kotis Konstantinos
Rehabilitation, School of Health Sciences, Metropolitan College, Thessaloniki, GRC.
Cultural Technology and Communication, Intelligent Systems Lab, University of the Aegean, Mytilene, GRC.
Cureus. 2024 Dec 27;16(12):e76496. doi: 10.7759/cureus.76496. eCollection 2024 Dec.
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition marked by movement hyperactivity, often persisting into adulthood. Understanding the movement patterns associated with ADHD is crucial for improving diagnostic precision and tailoring interventions. This study leverages the HYPERAKTIV dataset, which includes high-resolution temporal data on motor activity from people diagnosed with ADHD. We used the isolation forest algorithm to detect anomalies in activity data, followed by the development of a recurrent neural network (RNN) model to predict these anomalies over time. The RNN model demonstrated high predictive accuracy, with a mean accuracy of 0.953 and a mean loss of 0.124 for participants with ADHD. These findings suggest that machine learning techniques, particularly RNNs, can effectively identify and predict anomalies in temporal motor activity data, offering objective insights into ADHD-related movement behaviors. This approach is promising for informing personalized interventions and improving clinical decision-making in the management of ADHD.
注意力缺陷/多动障碍（ADHD）是一种常见的神经发育疾病，其特征为多动，这种症状往往会持续到成年期。了解与ADHD相关的运动模式对于提高诊断准确性和制定针对性干预措施至关重要。本研究利用了HYPERAKTIV数据集，该数据集包含了被诊断为ADHD的人群的高分辨率运动活动时间数据。我们使用孤立森林算法来检测活动数据中的异常，随后开发了一个循环神经网络（RNN）模型来预测这些异常随时间的变化。RNN模型显示出较高的预测准确率，ADHD参与者的平均准确率为0.953，平均损失为0.124。这些发现表明，机器学习技术，尤其是RNN，可以有效地识别和预测时间性运动活动数据中的异常，为与ADHD相关的运动行为提供客观见解。这种方法有望为ADHD管理中的个性化干预提供依据，并改善临床决策。
