基于SVM方法的神经网络呼吸音识别算法

提出了一种神经网络的SVM(支持向量机)呼吸音识别算法，将通过小波分析得到的呼吸音特征输入神经网络，作为SVM方法的特征输入，对训练样本进行训练，再对测试样本进行分类识别。对于呼吸音反映的3种状态（正常、轻度病变和重度病变）进行了识别，同时与K最近邻(KNN)方法进行比较。实验结果表明，SVM方法具有较高的识别精度，能够对呼吸音状态进行识别，同时在此领域也验证了在神经网络方法中无法避免的局部极值问题。提示基于SVM方法的神经网络呼吸音识别算法有较好的精度，可为身体局域网技术提供信息处理的有效算法。

Neural network recognition algorithm of breath sounds based on SVM

A SVM neural network (support vector machines) for breath sounds recognition algorithm was advanced, breath sounds feature obtained through wavelet analysis were input into neural networks and carried on the training to the training samples as a feature of SVM method input in order to classify the test samples. Three States (normal, mild and severe lesions) of breath sounds were recognized, and K nearest neighbor (KNN) methods are compared . The results show that SVM method has a higher recognition accuracy and can be used to recognize different breath sounds, which settled the local extremum problem that cannot be avoided in the neural network method and provide an effective algorithm for information processing in body area network technology.