基于心电图信号的测谎技术研究

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI:10.1109/ITNEC48623.2020.9084887

Xiaolong Li, Chaoyong Deng, Qiwei Wu, Ruirui Cui, Jintian Tang, Yandong Zhang

{"title":"基于心电图信号的测谎技术研究","authors":"Xiaolong Li, Chaoyong Deng, Qiwei Wu, Ruirui Cui, Jintian Tang, Yandong Zhang","doi":"10.1109/ITNEC48623.2020.9084887","DOIUrl":null,"url":null,"abstract":"Polygraph technology has always been concerned by the society. At present, there are many technologies used in the field of lie detection research. By summarizing the characteristics of the current technology, this paper proposes a multi-channel concealed polygraph technology based on ballistocardiogram (BCG), which can make up for the shortcomings of the existing technology. In this paper, the BCG signal, image signal, speech signal and galvanic skin response (GSR) signal are used to preprocess and extract the features. Using the extracted features to train the classification model, we can get an overall accuracy of 78%, and the area under the curve (AUC) score is 0.84. This experiment proves the effectiveness and advantages of BCG signal for lie detection, and also provides an effective method for the study of concealed polygraph technology.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research on polygraph technology based on ballistocardiogram signal\",\"authors\":\"Xiaolong Li, Chaoyong Deng, Qiwei Wu, Ruirui Cui, Jintian Tang, Yandong Zhang\",\"doi\":\"10.1109/ITNEC48623.2020.9084887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Polygraph technology has always been concerned by the society. At present, there are many technologies used in the field of lie detection research. By summarizing the characteristics of the current technology, this paper proposes a multi-channel concealed polygraph technology based on ballistocardiogram (BCG), which can make up for the shortcomings of the existing technology. In this paper, the BCG signal, image signal, speech signal and galvanic skin response (GSR) signal are used to preprocess and extract the features. Using the extracted features to train the classification model, we can get an overall accuracy of 78%, and the area under the curve (AUC) score is 0.84. This experiment proves the effectiveness and advantages of BCG signal for lie detection, and also provides an effective method for the study of concealed polygraph technology.\",\"PeriodicalId\":235524,\"journal\":{\"name\":\"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)\",\"volume\":\"117 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNEC48623.2020.9084887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNEC48623.2020.9084887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

测谎技术一直受到社会的关注。目前，有许多技术应用于测谎研究领域。在总结现有技术特点的基础上，提出了一种基于BCG的多通道隐蔽测谎技术，弥补了现有技术的不足。本文采用BCG信号、图像信号、语音信号和皮肤电反应(GSR)信号进行预处理和特征提取。利用提取的特征对分类模型进行训练，总体准确率为78%，曲线下面积(AUC)得分为0.84。实验证明了BCG信号在测谎中的有效性和优越性，也为隐蔽测谎技术的研究提供了一种有效的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Research on polygraph technology based on ballistocardiogram signal

Polygraph technology has always been concerned by the society. At present, there are many technologies used in the field of lie detection research. By summarizing the characteristics of the current technology, this paper proposes a multi-channel concealed polygraph technology based on ballistocardiogram (BCG), which can make up for the shortcomings of the existing technology. In this paper, the BCG signal, image signal, speech signal and galvanic skin response (GSR) signal are used to preprocess and extract the features. Using the extracted features to train the classification model, we can get an overall accuracy of 78%, and the area under the curve (AUC) score is 0.84. This experiment proves the effectiveness and advantages of BCG signal for lie detection, and also provides an effective method for the study of concealed polygraph technology.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

自引率

0.00%

发文量