ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States.
ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States.
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DOI：
10.3390/s23020937
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发表时间：
2023-01-13
期刊：
影响因子：
--
通讯作者：
Bagheri N
中科院分区：
文献类型：
--
作者：
Camara C;Peris-Lopez P;Safkhani M;Bagheri N
在过去的十年中，生物信号在设计新颖的生物识别系统时吸引了许多研究人员的注意。这些作品中的许多使用心脏信号及其表示为心电图（ECG）。如今，这些解决方案更加现实，因为我们可以通过使用可穿戴设备获取可靠的ECG记录。本文朝着这个方向发展，并提出了一种新的方法，心电图识别系统。为此，我们将ECG记录转换为Gramian角场（GAF）图像，这是一种在其他领域中众所周知但在生物信号中不太常见的时间序列编码技术。具体而言，使用极坐标变换时间序列，然后计算每对点的角度的余弦和。我们提出了一个概念验证识别系统，该系统基于使用这种方法的调谐VGG 19卷积神经网络。我们通过使用两个著名的公共数据集：MIT-BIH正常窦性心律数据库（受试者处于静息状态）和ECG-GUDB（四种特定活动下的个人）进行实验，证实了我们的建议的可行性。在这两种情况下，识别系统的准确率都达到了91%，错误接受率（FAR）是错误拒绝率（FRR）的8倍。
In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal’s feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR).
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影响因子：
2.9
作者：
Hejazi, Maryamsadat;Al-Haddad, S. A. R.;Aziz, Ahmad Fazli Abdul
通讯作者：
Aziz, Ahmad Fazli Abdul
影响因子：
5.1
作者：
Huang, Yuwen;Yang, Gongping;Yin, Yilong
通讯作者：
Yin, Yilong
影响因子：
3.9
作者：
Karimian, Nima;Tehranipoor, Mark;Forte, Domenic
通讯作者：
Forte, Domenic
一种基于卷积神经网络的新型二维心电图特征提取和分类算法，用于人体认证
DOI：
10.1016/j.future.2019.06.008
发表时间：
2019-12-01
影响因子：
7.5
作者：
Hammad, Mohamed;Zhang, Shanzhuo;Wang, Kuanquan
通讯作者：
Wang, Kuanquan
DOI：
10.3390/s17102228
发表时间：
2017-09-28
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Pinto JR;Cardoso JS;Lourenço A;Carreiras C
通讯作者：
Carreiras C