Hossen Abdulnasir
Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, P.O.Box 33, Al-Khoud, 123 Muscat, Oman.
Technol Health Care. 2013;21(4):345-56. doi: 10.3233/THC-130735.
Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremor with a certain overlap in the clinical presentation.
The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors using spectral analysis of tremor time-series recorded by accelerometry and surface EMG signals.
The Soft-Decision wavelet-based technique is to be used in this work in order to obtain a 16 bands approximate spectral representation of both accelerometer and two EMG signals of two sets of data (training and test). The training set consists of 21 ET subjects and 19 PD subjects while the test set consists of 20 ET and 20 PD subjects. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. A neural network of the type feed forward back propagation has been used to find the frequency bands associated with the different signals that yield better discrimination efficiency on training data. The same designed network is used to discriminate the test set.
Efficiency result of 87.5% was obtained using two different bands from each of the three signals under test.
The artificial neural network has been used successfully in both feature extraction and in pattern matching tasks in a complete classification system.
特发性震颤（ET）和帕金森病（PD）中的震颤是两种最常见的病理性震颤，临床表现有一定重叠。
本研究的主要目的是使用人工神经网络选择最佳特征，并通过对加速度计和表面肌电信号记录的震颤时间序列进行频谱分析，区分这两种类型的震颤。
本研究将使用基于软决策小波的技术，以获得两组数据（训练和测试）的加速度计以及两个肌电信号的16波段近似频谱表示。训练集包括21名ET受试者和19名PD受试者，而测试集包括20名ET和20名PD受试者。这些数据是在德国基尔大学神经病学系为诊断目的而记录的。使用前馈反向传播类型的神经网络来找到与不同信号相关的频段，这些频段在训练数据上具有更好的区分效率。使用相同设计的网络对测试集进行区分。
使用测试的三个信号中的每一个的两个不同频段，获得了87.5%的效率结果。
在一个完整的分类系统中，人工神经网络已成功用于特征提取和模式匹配任务。
