Rodríguez-Martín Daniel, Samà Albert, Pérez-López Carlos, Català Andreu, Moreno Arostegui Joan M, Cabestany Joan, Bayés Àngels, Alcaine Sheila, Mestre Berta, Prats Anna, Crespo M Cruz, Counihan Timothy J, Browne Patrick, Quinlan Leo R, ÓLaighin Gearóid, Sweeney Dean, Lewy Hadas, Azuri Joseph, Vainstein Gabriel, Annicchiarico Roberta, Costa Alberto, Rodríguez-Molinero Alejandro
Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Technical Research Centre for Dependency Care and Autonomous Living (CETPD), Vilanova i la Geltrú, Spain.
Sense4Care, Barcelona, Spain.
PLoS One. 2017 Feb 15;12(2):e0171764. doi: 10.1371/journal.pone.0171764. eCollection 2017.
Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.
在帕金森病（PD）的症状中，步态冻结（FoG）是最使人衰弱的症状之一。为了评估步态冻结，目前的临床实践大多基于问卷调查在数周和数月内进行反复评估，而这可能无法准确反映该症状的严重程度。使用非侵入性系统来监测日常生活活动（ADL）以及患者全天所经历的帕金森病症状，可以对步态冻结进行更准确、客观的评估，以便更好地了解疾病的发展，并在调整患者治疗方案时做出更明智的决策。本文提出了一种新算法，该算法基于支持向量机（SVM）和佩戴在腰部的单个三轴加速度计，采用机器学习方法来检测步态冻结。通过在门诊环境中收集的21名帕金森病患者在家中的加速度信号对该方法进行评估，并在两种不同条件下进行评估：第一，使用留一法测试通用模型；第二，使用个性化模型，该模型还使用了每个患者数据集的一部分。结果表明，与通用模型相比，个性化模型的准确率有显著提高，特异性和敏感性几何平均值（GM）提高了7.2%。此外，将所采用的支持向量机方法与目前使用的最全面的步态冻结检测方法（本文中称为MBFA）进行了比较。我们新的通用方法的结果表明，与MBFA通用模型相比，GM提高了11.2%；对于个性化模型，相对于MBFA个性化模型提高了10%。因此，我们的结果表明，机器学习方法可用于监测帕金森病患者日常生活中的步态冻结，此外，用于步态冻结检测的个性化模型可用于提高监测准确性。
