不同学习方法下运动和休息情境下的ECG识别
ECG Identification under Exercise and Rest Situations via Various Learning Methods
摘要：
随着信息安全技术的发展，人工识别作为其核心技术，近年来受到越来越多的关注。大量的生物特征，包括指纹、人脸、虹膜，已经被应用于安全系统，这些系统有时被认为容易受到伪造和欺骗攻击。心电图由于其制作的困难而备受关注。虽然许多研究已经表明心电图具有很好的身份识别能力，但目前的心电图身份识别研究大多集中在静息状态下。本文克服了以往研究的过于简单化，对运动和休息两种情况下的表现进行了评价，特别是运动对ECGID的影响。通过将各种现有的学习方法应用于我们的ECG数据集，我们发现，目前的方法，可以很好地支持个人的识别休息，不足以呈现令人满意的ECGID性能在运动情况下，从而暴露出现有的ECG识别方法的不足。
英文摘要：
As the advancement of information security, human recognition as its core technology, has absorbed an increasing amount of attention in the past few years. A myriad of biometric features including fingerprint, face, iris, have been applied to security systems, which are occasionally considered vulnerable to forgery and spoofing attacks. Due to the difficulty of being fabricated, electrocardiogram (ECG) has attracted much attention. Though many works have shown the excellent human identification provided by ECG, most current ECG human identification (ECGID) researches only focus on rest situation. In this manuscript, we overcome the oversimplification of previous researches and evaluate the performance under both exercise and rest situations, especially the influence of exercise on ECGID. By applying various existing learning methods to our ECG dataset, we find that current methods which can well support the identification of individuals under rests, do not suffice to present satisfying ECGID performance under exercise situations, therefore exposing the deficiency of existing ECG identification methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
