Objective identification and analysis of physiological and behavioral signs of schizophrenia.
Objective identification and analysis of physiological and behavioral signs of schizophrenia.
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DOI：
10.3109/09638237.2015.1019048
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Clifford GD
中科院分区：
文献类型：
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作者：
Osipov M;Behzadi Y;Kane JM;Petrides G;Clifford GD
背景：精神科医生经常使用患者的身体活动来帮助诊断精神障碍。通常情况下，它主要基于自我报告或观察，而很少基于活动记录。尽管自主神经系统经常受到精神障碍的影响，但很少使用与生理相关的其他信号。目的：本研究试图融合生理和身体活动数据，发现精神分裂症的预测特征。方法：对16例精神分裂症患者和19例健康对照者进行连续同步心率（HR）和体力活动记录。分析了记录数据的统计特征，以及非线性休息-活动测量和无序测量。结果：确定了运动活动的标准差和模式、HR信号的多尺度熵随尺度变化的动态和平均HR四个最具预测力的特征。根据这些特征训练的分类器在区分精神分裂症患者和对照组方面的交叉验证准确率为95.3% (AUC = 0.99)，而仅使用HR或运动活动特征的准确率分别为78.5和85.5% （AUC = 0.85和AUC = 0.90）。结论：生理和身体活动信号为心理健康评估提供了补充信息。
Background: A patient’s physical activity is often used by psychiatrists to contribute to the diagnostic process for mental disorders. Typically, it is based mostly on self-reports or observations, and hardly ever upon actigraphy. Other signals related to physiology are rarely used, despite the fact that the autonomic nervous system is often affected by mental disorders. Aim: This study attempted to fuse physiological and physical activity data and discover features that are predictive for schizophrenia. Method: Continuous simultaneous heart rate (HR) and physical activity recordings were made on 16 individuals with schizophrenia and 19 healthy controls. Statistical characteristics of the recorded data were analyzed, as well as non-linear rest–activity measures and disorganization measures. Results: Four most predictive features for schizophrenia were identified, namely, the standard deviation and mode of locomotor activity, dynamics of Multiscale Entropy change over scales of HR signal and the mean HR. A classifier trained on these features provided a cross-validation accuracy of 95.3% (AUC = 0.99) for differentiating between schizophrenia patients and controls, compared to 78.5 and 85.5% accuracy (AUC = 0.85 and AUC = 0.90) using only the HR or locomotor activity features. Conclusion: Physiological and physical activity signals provide complimentary information for assessment of mental health.