Personal Identification by Convolutional Neural Network with ECG Signal

被引：0

作者：

Xu, Jianbo ^{[1
]}

Li, Tianhui ^{[1
]}

Chen, Ying ^{[1
]}

Chen, Wenxi ^{[1
]}

机构：

[1] Univ Aizu, Biomed Informat Technol Lab, Aizu Wakamatsu, Fukushima, Japan

来源：

2018 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC) | 2018年

关键词：

electrocardiogram; bathing; drowning accidents; personal identification; convolutional neural network; RECOGNITION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a novel approach for personal biometric identification with Convolutional Neural Network (CNN) based on the electrocardiogram (ECG) signals that are measured during bathing, aiming at exploring a feasible way to avoid or reduce the drowning accidents during daily bathing. After we perform denoising and QRS segmentation on the raw ECG signal, the preliminary experimental results showed that the best and robust identification rate is 97.20% for 10 individuals only with 5 epochs. When the proposed method was tested on a public ECG Database, the identification rate is as high as 98.70% This work provides with strong evidence that ECG signals can be useful for personal identification.

引用

页码：559 / 563

页数：5

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