基于PCANet的脉搏信号亚健康检测

Pulse signal sub-health detection based on PCANet

目前亚健康状态识别中脉搏信号特征提取困难,且多依赖于手工提取特征而影响识别率。针对这一问题,本文提出了一种基于主成分分析网络(Principal Component Analysis Network,PCANet)的脉搏信号亚健康检测新方法。首先对预处理的脉搏信号进行特征提取;其次将这些特征进行哈希编码,直方图分块,作为特征描述;然后使用分类器将健康和亚健康的两类脉搏信号进行分类识别,并与传统特征提取方法的分类效果进行比较。实验结果表明本文方法对亚健康状态识别达到了较高的准确率,相比传统的特征提取方法,PCANet方法在识别率上提高了10%以上,因此,本文所提出的方法能够有效地区分健康与亚健康状态,为亚健康状态的检测提供了一种新的参考依据。

Now pulse signal feature is difficult to be extracted in the sub-health detecti on,and it is easy to affect recognition accuracy due to the relying on mainly hand-crafted feature e xtraction.To solve these problems,this paper proposes a pulse signal sub-health detection method based o n principal component analysis network (PCANet).Firstly,PCANet is used to extract features from prep rocessing pulse signal in the experiment.Then,we deal these features with Hash code,histogram block as the description features.Finally,the feature vectors are fed into two classifiers to classify the healthy and sub-healthy subjects.The test results is compared with the results from other methods of traditional features extraction.The experimental analysis show that our method obtains the highest accuracy rate in sub-health recognition field.The recognition rate of PCANet method is improveed by 10% compared with those of traditional feature extraction methods.So our approach can effectively distinguish health and sub-health subjects,and provides a new reference for human detection of sub -health state.