Butt A H, Cavallo F, Maremmani C, Rovini E
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5761-5764. doi: 10.1109/EMBC44109.2020.9176051.
Nowadays objective and efficient assessment of Parkinson Disease (PD) with machine learning techniques is a major focus for clinical management. This work presents a novel approach for classification of patients with PD (PwPD) and healthy controls (HC) using Bidirectional Long Short-Term Neural Network (BLSTM). In this paper, the SensHand and the SensFoot inertial wearable sensors for upper and lower limbs motion analysis were used to acquire motion data in thirteen tasks derived from the MDS-UPDRS III. Sixty-four PwPD and fifty HC were involved in this study. One hundred ninety extracted spatiotemporal and frequency parameters were applied as a single input against each subject to develop a recurrent BLSTM to discriminate the two groups. The maximum achieved accuracy was 82.4%, with the sensitivity of 92.3% and specificity of 76.2%. The obtained results suggest that the use of the extracted parameters for the development of the BLSTM contributed significantly to the classification of PwPD and HC.
如今，利用机器学习技术对帕金森病（PD）进行客观有效的评估是临床管理的一个主要重点。这项工作提出了一种使用双向长短期神经网络（BLSTM）对帕金森病患者（PwPD）和健康对照者（HC）进行分类的新方法。在本文中，用于上肢和下肢运动分析的SensHand和SensFoot惯性可穿戴传感器被用于获取源自MDS-UPDRS III的13项任务中的运动数据。本研究纳入了64名帕金森病患者和50名健康对照者。将提取的190个时空和频率参数作为单个输入应用于每个受试者，以开发一个循环BLSTM来区分这两组。最高准确率达到82.4%，敏感性为92.3%，特异性为76.2%。所得结果表明，使用提取的参数来开发BLSTM对帕金森病患者和健康对照者的分类有显著贡献。
