Faculty of Engineering, Institute of Electrical and Information Engineering, Digital Signal Processing and System Theory, Kiel University, Kiel, Germany.
Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany.
Biomed Tech (Berl). 2022 Feb 24;67(2):119-130. doi: 10.1515/bmt-2021-0140. Print 2022 Apr 26.
The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson's Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.
诊断震颤患者的过程常常导致误诊。因此，需要现有的医学学术分析震颤的技术方法来更有效地区分不同的疾病。为此，已经开发了一种实时分类测量震颤信号的系统。为此，在不同的手部位置测量了 561 名受试者的手部震颤。在检查过程中记录加速度和表面肌电图。在这项研究中，考虑了帕金森病、特发性震颤和生理性震颤患者的数据。在信号分析的第一步中进行特征提取，并检查得到的特征的判别值。在第二步中，基于不同的模式识别技术开发了三个分类模型，以根据震颤类型对受试者进行分类。使用训练好的决策树，三种震颤类型的相对诊断准确率为 83.14%。神经网络的准确率为 84.24%，两种分类器的组合的准确率为 85.76%。该方法很有前景，涉及记录时间序列的更多特征将提高判别值。
