Nowadays, the method of simple-feature extraction has been extensively studied and is used in PPG biometric recognition; some promising results have been reported. However, some useful information is often lost in the process of PPG signal denoising; the time-domain, frequency-domain, or wavelet feature extracted is often partial, which cannot fully express the raw PPG signal; and it is also difficult to choose the appropriate matching method. Therefore, to make up for these shortcomings, a method of PPG biometric recognition based on multifeature extraction and naive Bayes classifier is proposed. First, in the preprocessing of the raw PPG data, the sliding window method is used to rerepresent the raw data. Second, the feature-extraction methods based on time-domain, frequency-domain, and wavelet are analysed in detail, then these methods are used to extract the time-domain, frequency-domain, and wavelet features, and the features are concatenated into a multifeature. Finally, the multifeature is normalized and combined with classifiers and Euclidean distance for matching and decision-making. Extensive experiments are conducted on three PPG datasets, it is found that the proposed method can achieve a recognition rate of 98.65%, 97.76%, and 99.69% on the respective sets, and the results demonstrate that the proposed method is not inferior to several state-of-the-art methods.

中文翻译：

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基于多特征提取和朴素贝叶斯分类器的PPG生物特征识别研究

如今，简单特征提取方法已得到广泛研究，并用于PPG生物特征识别。报道了一些令人鼓舞的结果。但是，在PPG信号降噪过程中常常会丢失一些有用的信息。提取的时域，频域或小波特征常常是局部的，不能完全表达原始的PPG信号。而且很难选择合适的匹配方法。因此，为弥补这些不足，提出了一种基于多特征提取和朴素贝叶斯分类器的PPG生物特征识别方法。首先，在原始PPG数据的预处理中，使用滑动窗口方法重新表示原始数据。其次，详细分析了基于时域，频域和小波的特征提取方法，然后使用这些方法提取时域，频域和小波特征，然后将这些特征串联成一个多重特征。最后，将多重特征归一化并与分类器和欧氏距离相结合，以进行匹配和决策。在三个PPG数据集上进行了广泛的实验，发现该方法在各个集合上的识别率分别为98.65％，97.76％和99.69％，结果表明该方法不亚于几种状态最先进的方法。