Cancela J, Pansera M, Arredondo M T, Estrada J J, Pastorino M, Pastor-Sanz L, Villalar J L
Life Supporting Technologies, Technical University of Madrid (UPM), 28040, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1008-11. doi: 10.1109/IEMBS.2010.5627775.
The current work describes a methodology to automatically detect the severity of bradykinesia in motor disease patients using wireless, wearable accelerometers. This methodology was tested with cross validation through a sample of 20 Parkinson's disease patients. The assessment of methodology was carried out through some daily living activities which were detected using an activity recognition algorithm. The Unified Parkinson's Disease Rating Scale (UPDRS) severity classification of the algorithm coincides between 70 and 86% from that of a trained neurologist depending on the classifier used. These severities were calculated for 5 second segments of the signal with 50% of overlap. A bradykinesia profiler is also presented in this work. This profiler removes the overlap of the segments and calculates the confidence of the resulting events. It also calculates average severity, duration and symmetry values for those events. The profiler has been tested with a bogus dataset. Future work includes better training for the severity classifier with a larger sample and testing the profiler with real, longterm patient data in a projected pilot phase in three European hospitals.
当前的工作描述了一种使用无线可穿戴加速度计自动检测运动疾病患者运动迟缓严重程度的方法。该方法通过对20名帕金森病患者的样本进行交叉验证测试。该方法的评估是通过一些使用活动识别算法检测到的日常生活活动来进行的。根据所使用的分类器，该算法的统一帕金森病评定量表（UPDRS）严重程度分类与训练有素的神经科医生的分类之间的一致性在70%至86%之间。这些严重程度是针对信号的5秒段计算的，重叠率为50%。这项工作还展示了一个运动迟缓剖析器。该剖析器消除了段的重叠，并计算了所得事件的置信度。它还计算这些事件的平均严重程度、持续时间和对称值。该剖析器已使用一个虚拟数据集进行了测试。未来的工作包括使用更大的样本对严重程度分类器进行更好的训练，并在预计的三个欧洲医院的试点阶段使用真实的长期患者数据对剖析器进行测试。
