A novel feature ranking algorithm for biometric recognition with PPG signals
A novel feature ranking algorithm for biometric recognition with PPG signals
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DOI：
10.1016/j.compbiomed.2014.03.005
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发表时间：
2014-06-01
影响因子：
7.7
通讯作者：
Bozkurt, M. Recep
中科院分区：
文献类型：
--
作者：
Kavsaoglu, A. Resit;Polat, Kemal;Bozkurt, M. Recep
本研究旨在描述光电体积描记法 (PPG) 信号的应用以及从其一阶和二阶导数获取的时域特征在生物特征识别中的应用。为此，总共提取了 40 个特征，并提出了特征排序算法。该算法计算每个特征对生物特征识别的贡献，并对特征进行配置，其贡献由大到小。在识别特征的贡献时，使用欧氏距离和绝对距离公式。特征的 k-NN（k 最近邻）分类器应用的结果证明了所提出算法的效率。在应用过程中，来自 30 名健康受试者中每一位的属于两个不同持续时间的每个 15 周期 PPG 信号均与 PPG 数据采集卡一起使用。从受试者记录的第一个 PPG 信号被评估为第一个配置；稍后在不同时间记录的 PPG 信号作为第二配置，并且将两者的组合评估为第三配置。当对与所提出的算法一起创建的 k-NN 分类器模型的结果进行评估时，第一配置的识别率为 90.44%，第二配置的识别率为 94.44%，第三配置的识别率为 87.22%。获得的结果表明，所提出的算法和基于所开发的 PPG 信号的生物特征识别模型对于使用所提出的方法进行非接触式识别非常有前景。 (C) 2014 Elsevier Ltd. 保留所有权利。
This study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.