Schneider Jakub, Bakštein Eduard, Kolenič Marian, Vostatek Pavel, Correll Christoph U, Novák Daniel, Španiel Filip
Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.
Applied Neuroscience and Neuroimaging, National Institute of Mental Health, Klecany, Czech Republic.
CNS Spectr. 2022 Feb;27(1):82-92. doi: 10.1017/S1092852920001777. Epub 2020 Sep 4.
Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls (HCs) into their respective groups.
Ninety-day actigraphy records from 25 interepisode BD patients (ie, Montgomery-Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) < 15) and 25 sex- and age-matched HCs were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HCs. Mean values and time variations of a set of standard actigraphy features were analyzed and further validated using the random forest classifier.
Using all actigraphy features, this method correctly assigned 88% (sensitivity = 85%, specificity = 91%) of BD patients and HCs to their respective group. The classification success may be confounded by differences in employment between BD patients and HCs. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen's d = 1.33), 79% of the subjects (sensitivity = 76%, specificity = 81%) were correctly classified.
A machine-learning actigraphy-based model was capable of distinguishing between interepisode BD patients and HCs solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HCs while being less affected by employment status.
双相情感障碍（BD）与昼夜节律紊乱有关，导致异常的运动活动模式。我们旨在探讨通过纵向活动记录仪评估的单纯运动活动是否可用于将BD患者和健康对照（HCs）准确分类到各自的组中。
使用25例发作间期BD患者（即蒙哥马利-阿斯伯格抑郁评定量表（MADRS）和杨氏躁狂评定量表（YMRS）<15）以及25例性别和年龄匹配的HCs的90天活动记录仪记录，以识别能够区分BD患者和HCs的潜在活动记录仪生物标志物。分析了一组标准活动记录仪特征的平均值和时间变化，并使用随机森林分类器进行了进一步验证。
使用所有活动记录仪特征，该方法将88%（敏感性=85%，特异性=91%）的BD患者和HCs正确分类到各自的组中。分类成功率可能因BD患者和HCs之间就业情况的差异而混淆。当使用不受就业状态影响的运动活动特征时（最强的特征是日内变异性的时间变化，Cohen's d = 1.33），79%的受试者（敏感性=76%，特异性=81%）被正确分类。
基于活动记录仪的机器学习模型仅根据运动活动就能区分发作间期BD患者和HCs。即使省略受就业状态影响的特征，分类仍然有效。研究结果表明，活动记录仪参数的时间变异性可能为区分BD患者和HCs提供判别力，同时受就业状态的影响较小。
