[论文解读] Characterizing and Detecting Freezing of Gait using Multi-modal Physiological Signals

Ying Wang, Floris Beuving+5 more|arXiv (Cornell University)|Sep 26, 2020

Muscle activation and electromyography studies参考文献 37被引用 5

一句话总结

本研究提出了一种基于多模态生理信号的方法，通过检测帕金森病患者转身时的眼动稳定速度和下肢震颤作为关键特征，实现步态冻结（FOG）的检测。采用留一被试者交叉验证，该方法在敏感度达到97%、特异度达到96%的同时，曲线下面积（AUPRC）达到0.94，优于单模态方法。

ABSTRACT

Freezing-of-gait a mysterious symptom of Parkinsons disease and defined as a sudden loss of ability to move forward. Common treatments of freezing episodes are currently of moderate efficacy and can likely be improved through a reliable freezing evaluation. Basic-science studies about the characterization of freezing episodes and a 24/7 evidence-support freezing detection system can contribute to the reliability of the evaluation in daily life. In this study, we analyzed multi-modal features from brain, eye, heart, motion, and gait activity from 15 participants with idiopathic Parkinsons disease and 551 freezing episodes induced by turning in place. Statistical analysis was first applied on 248 of the 551 to determine which multi-modal features were associated with freezing episodes. Features significantly associated with freezing episodes were ranked and used for the freezing detection. We found that eye-stabilization speed during turning and lower-body trembling measure significantly associated with freezing episodes and used for freezing detection. Using a leave-one-subject-out cross-validation, we obtained a sensitivity of 97%+/-3%, a specificity of 96%+/-7%, a precision of 73%+/-21%, a Matthews correlation coefficient of 0.82+/-0.15, and an area under the Precision-Recall curve of 0.94+/-0.05. According to the Precision-Recall curves, the proposed freezing detection method using the multi-modal features performed better than using single-modal features.

研究动机与目标

• 识别与帕金森病患者步态冻结（FOG）相关联的生理标志物。
• 利用多模态生理信号开发一种可靠且可实时运行的FOG检测系统，以实现持续监测。
• 通过整合来自大脑、眼睛、心脏、运动和步态活动的信号，改进现有FOG检测方法。
• 评估多模态特征融合在FOG检测中相对于单模态方法的性能表现。

提出的方法

• 从15名帕金森病患者中采集包括眼动、下肢运动、心率和步态活动在内的多模态生理信号。
• 通过原地转身任务诱发551次FOG事件，以实现受控分析。
• 对248次事件进行统计分析，识别与FOG显著相关的特征。
• 选取转身时的眼动稳定速度和下肢震颤作为区分性特征。
• 采用留一被试者交叉验证评估检测性能。
• 使用精确率、召回率、F1分数、马修斯相关系数以及精确率-召回率曲线下面积（AUPRC）评估检测性能。

实验结果

研究问题

• RQ1 哪些多模态生理特征与帕金森病患者的步态冻结事件显著相关？
• RQ2 转身时的眼动稳定速度能否作为FOG检测的可靠生物标志物？
• RQ3 多模态特征融合的性能与单模态检测在FOG识别中的表现相比如何？
• RQ4 在留一被试者交叉验证框架下，所提出的检测系统的敏感度和特异度分别是多少？

主要发现

• 转身时的眼动稳定速度和下肢震颤与步态冻结事件显著相关。
• 多模态检测系统实现了97% ± 3%的敏感度和96% ± 7%的特异度。
• 精确率为73% ± 21%，表明在高敏感度条件下阳性预测值中等。
• 马修斯相关系数为0.82 ± 0.15，表明整体分类性能优异。
• 精确率-召回率曲线下面积为0.94 ± 0.05，表明其性能优于单模态方法。
• 所提出的方法在FOG检测中优于单模态特征集，证实了多模态融合的价值。