基于PCNN-LSTM神经网络的膝关节摆动信号分类识别

膝关节摆动(VAG)信号是指膝关节屈曲或伸展时发出的声音或振动信号,可灵敏、客观地描述膝关节的健康状态,在膝关节疾病的无创检测中具有重要作用.现有的对VAG信号正常和异常分类方法自动化程度低,且分类准确度较低,总体性能有待进一步提升.因此,提出一种基于改进卷积神经循环网络(PCNN-LSTM)的VAG信号分类算法.首先...

Classification of Vibroarthrographic SignalsBased on PCNN-LSTM Neural Network

The vibroarthrographic (VAG) signal is a sound of the knee joint during flexion and extension. VAG signal can be used to describe the health status of knee joint sensitively and objectively. Hence, it is often used in the detection of knee joint diseases. However, at present, classification accuracy of the normal and abnormal classification method of VAG signal is still low and not automated. The performance needs to be further improved. To solve this problem, in this paper, a classification algorithm of VAG signal based on improved convolutional neural network (PCNN-LSTM) was proposed. First, empirical mode decomposition (EMD) and wavelet transform are used to transform one-dimensional VAG signal into two-dimensional time-frequency characteristic spectrum, which was used as data set. Second, on the basis of CNN, the parallel CNN network structure was combined with LSTM neural network to form the PCNN-LSTM model, which could classify normal or abnormal VAG signals and realizd the automatic detection of knee joint health status. In this paper, the performances of the proposed algorithm were verified by the data set that composed of the real VAG signals collected by the acceleration sensor (181A02) and USB acquisition instrument (FSC812). The data set consisted of 654 samples, including 222 health data and 432 data of patients with knee diseases. Results showed that the classification accuracy of the proposed algorithm was 96.93%, the sensitivity was 100%, and the specificity was 95.56%. Compared with other algorithms, the proposed algorithm achieved better results, and realized the classification and recognition of VAG signals, which was of great significance for non-invasive detection and auxiliary diagnosis of knee joint diseases.