使用可穿戴设备和机器学习预测帕金森病的步态冻结。

Prediction of Freezing of Gait in Parkinson's Disease Using Wearables and Machine Learning.

机构信息

Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy.

Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy.

出版信息

Sensors (Basel). 2021 Jan 17;21(2):614. doi: 10.3390/s21020614.

DOI:10.3390/s21020614

PMID:33477323

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7830634/

Abstract

UNLABELLED

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson's disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG.

METHODS

A cohort of 11 Parkinson's disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes.

RESULTS

The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy.

CONCLUSIONS

Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm's effectiveness.

摘要

目的：提出一种能够捕捉到冻结步态（FOG）发作前行走模式典型退化的可穿戴系统，使用机器学习算法实现可靠的 FOG 预测，并验证多巴胺能治疗是否会影响我们的系统检测和预测 FOG 的能力。

方法：招募了 11 名正在接受（ON）和不接受（OFF）多巴胺能治疗的帕金森病患者，在他们的每只小腿上放置了两个惯性传感器，并要求他们进行计时起立行走测试。我们对角速度信号进行了一步一步的分割，并从时间和频率域提取了后续特征。我们采用封装方法进行特征选择，并优化了不同的机器学习分类器，以捕捉 FOG 和预 FOG 发作。

结果：在患者 ON 和 OFF 治疗的情况下，实现的 FOG 检测算法在留一受试者外验证中表现出色。对于预 FOG 检测，实现的分类算法在留一受试者外验证中，ON 治疗患者的敏感性为 84.1%（85.5%），特异性为 85.9%（86.3%），准确性为 85.5%（86.1%）；OFF 治疗患者的敏感性为 84.0%（56.6%），特异性为 88.3%（92.5%），准确性为 87.4%（86.3%）。当分类模型使用 ON（OFF）治疗患者的数据进行训练，然后在 OFF（ON）治疗患者中进行测试时，我们发现敏感性为 84.0%（56.6%），特异性为 88.3%（92.5%），准确性为 87.4%（86.3%）。

结论：机器学习模型能够以足够的准确性预测 FOG 的发生。多巴胺能治疗会影响预 FOG 步态模式，从而影响算法的有效性。