Cole Bryan T, Roy Serge H, De Luca Carlo J, Nawab S
Dept. of Electrical and Computer Engineering (ECE), Boston University, MA 02215, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6062-5. doi: 10.1109/IEMBS.2010.5627618.
We present a dynamic neural network (DNN) solution for detecting time-varying occurrences of tremor and dyskinesia at 1 s resolution from time series data acquired from surface electromyographic (sEMG) sensors and tri-axial accelerometers worn by patients with Parkinson's disease (PD). The networks were trained and tested on separate datasets, each containing approximately equal proportions of tremor, dyskinesia, and disorder-free data from 8 PD and 4 control subjects performing unscripted and unconstrained activities in an apartment-like environment. During DNN testing, tremor was detected with a sensitivity of 93% and a specificity of 95%, while dyskinesia was detected with a sensitivity of 91% and a specificity of 93%. Similar sensitivity and specificity levels were obtained when DNN testing was carried out on subjects who were not included in DNN training.
我们提出了一种动态神经网络（DNN）解决方案，用于从帕金森病（PD）患者佩戴的表面肌电图（sEMG）传感器和三轴加速度计采集的时间序列数据中，以1秒的分辨率检测震颤和异动症的时变发作情况。这些网络在单独的数据集上进行训练和测试，每个数据集包含来自8名PD患者和4名对照受试者在类似公寓环境中进行无脚本、无约束活动时的震颤、异动症和无疾病数据，且比例大致相等。在DNN测试期间，震颤检测的灵敏度为93%，特异性为95%，而异动症检测的灵敏度为91%，特异性为93%。对未纳入DNN训练的受试者进行DNN测试时，也获得了类似的灵敏度和特异性水平。
