Background

Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques available to automate this process for Parkinson's disease (PD) patients with freezing of gait (FOG).

Objective

To determine the validity of a data-driven approach for automated gait event detection.

Methods

15 freezers were asked to complete several straight-line and 360 degree turning trials in a 3D gait laboratory during the off-period of their medication cycle. Trials that contained a freezing episode were indicated as freezing trials (FOG) and trials without a freezing episode were termed as functional gait (FG). Furthermore, the highly varied gait data between onset and termination of a FOG episode was excluded. A Temporal Convolutional Neural network (TCN) was trained end-to-end with lower extremity kinematics. A Bland-Altman analysis was performed to evaluate the agreement between the results of the proposed model and the manual annotations.

Results

For FOG-trials, F1 scores of 0.995 and 0.992 were obtained for IC and EC, respectively. For FG-trials, F1 scores of 0.997 and 0.999 were obtained for IC and EC, respectively. The Bland-Altman plots indicated excellent timing agreement, with on average 39% and 47% of the model predictions occurring within 10 ms from the manual annotations for FOG-trials and FG-trials, respectively.

Significance

These results indicate that our data-driven approach for detecting gait events in PD patients with FOG is sufficiently accurate and reliable for clinical applications.

中文翻译：

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一种数据驱动的方法，用于检测帕金森氏症患者转身和步态冻结期间的步态事件。

背景

手动标注初始触点（IC）和最终触点（EC）是一个耗时的过程。当前，尚无鲁棒的技术可用于使步态冻结（FOG）的帕金森氏病（PD）患者自动化。

目的

确定自动步态事件检测的数据驱动方法的有效性。

方法

要求15个冰柜在服药周期外的3D步态实验室中完成几个直线和360度转向试验。包含冰冻发作的试验称为冰冻试验（FOG），无冰冻发作的试验称为功能步态（FG）。此外，FOG发作的开始和结束之间的步态数据差异很大。使用下肢运动学端对端训练了时间卷积神经网络（TCN）。进行了Bland-Altman分析，以评估所提出模型的结果与手动注释之间的一致性。

结果

对于FOG试验，IC和EC的F1评分分别为0.995和0.992。对于FG试验，IC和EC的F1得分分别为0.997和0.999。Bland-Altman图显示了极好的时序一致性，平均39％和47％的模型预测分别发生在FOG试用版和FG试用版的手动注释后10毫秒之内。

意义

这些结果表明，我们的数据驱动方法可用于检测FOG的PD患者的步态事件，对于临床应用而言是足够准确和可靠的。