重性抑郁障碍
康复
物理医学与康复
功能近红外光谱
萧条(经济学)
前额叶腹外侧皮质
前额叶皮质
背外侧前额叶皮质
可穿戴计算机
心理学
医学
物理疗法
认知
精神科
计算机科学
经济
嵌入式系统
宏观经济学
作者
Yibo Zhu,Jagadish K. Jayagopal,Ranjana K. Mehta,Madhav Erraguntla,Joseph Nuamah,Anthony D. McDonald,Heather B. Taylor,Shuo-Hsiu Chang
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering
[Institute of Electrical and Electronics Engineers]
日期:2020-04-01
卷期号:28 (4): 961-969
被引量:38
标识
DOI:10.1109/tnsre.2020.2972270
摘要
Major depressive disorder (MDD) has shown to negatively impact physical recovery in a variety of medical events (e.g., stroke and spinal cord injuries). Yet depression assessments, which are typically subjective in nature, are seldom considered to develop or guide rehabilitation strategies. The present study developed a predictive depression assessment technique using functional near-infrared spectroscopy (fNIRS) that can be rapidly integrated or performed concurrently with existing physical rehabilitation tasks. Thirty-one volunteers, including 14 adults clinically diagnosed with MDD and 17 healthy adults, participated in the study. Brain oxy-hemodynamic (HbO) responses were recorded using a 16-channel wearable continuous-wave fNIRS device while the volunteers performed the Grasp and Release Test in four 16-minute blocks. Ten features, extracted from HbO signals, from each channel served as inputs to XGBoost and Random Forest algorithms developed for each block and combination of successive blocks. Top 5 common features resulted in a classification accuracy of 92.6%, sensitivity of 84.8%, and specificity of 91.7% using the XGBoost classifier. This study identified mean HbO, full width half maximum and kurtosis, as specific neuromarkers, for predicting MDD across specific depression-related regions of interests (i.e., dorsolateral and ventrolateral prefrontal cortex). Our results suggest that a wearable fNIRS head probe monitoring specific brain regions, and limiting extraction to few features, can enable quick setup and rapid assessment of depression in patients. The overarching goal is to embed predictive neurotechnology during post-stroke and post-spinal-cord-injury rehabilitation sessions to monitor patients' depression symptomology so as to actively guide decisions about motor therapies.
科研通智能强力驱动
Strongly Powered by AbleSci AI