Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Electrocardiogram01:29

Electrocardiogram

2.3K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
2.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

<i>In silico</i> cardiac safety assessment using a multi-biomarker approach based on an electrophysiological model of hiPSC-derived cardiomyocytes.

Toxicological research·2026
Same author

Improving biomarker robustness for in silico cardiac safety assessment through an excitation-contraction coupling model.

Toxicology and applied pharmacology·2026
Same author

Integrating high-fidelity hiPSC-cardiomyocytes with AI-driven modeling for enhanced proarrhythmic risk assessment.

Archives of toxicology·2026
Same author

Incorporating inter-individual variability to improve the reliability of predicted outcomes in in silico cardiac safety assessment.

Toxicology and applied pharmacology·2026
Same author

ToxCML: A Hybrid mfCoQ-RASAR-Based Platform Integrating Consensus QSAR and Read-Across for Comprehensive Multi-End Point Toxicity Assessment.

Journal of chemical information and modeling·2026
Same author

Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced multi-endpoint toxicity assessment.

Toxicology mechanisms and methods·2026

相关实验视频

Updated: Jun 20, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.2K

单一心跳ECG认证:一个1D-CNN框架,用于强大而高效的人类识别.

Ana Rahma Yuniarti1,2, Syamsul Rizal1,3, Ki Moo Lim1,4,5

  • 1Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, Republic of Korea.

Frontiers in bioengineering and biotechnology
|July 19, 2024
PubMed
概括

一个新的一维卷积神经网络 (1D-CNN) 框架有效地使用单个心电图 (ECG) 心跳来验证个人的身份. 这种强大的系统实现了近乎完美的准确性,证明了其对安全,实时应用的潜力.

关键词:
1D-CNN 1D-CNN 是一个数字.在SMOTE中使用.认证的真实性 认证的真实性生物识别信息 生物识别信息卷积神经网络是一种卷积神经网络.电心电图 (ECG) 是一种心电图.标识 标识 标识 标识 标识一个心跳,一个心跳.

更多相关视频

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

455
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.6K

相关实验视频

Last Updated: Jun 20, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.2K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

455
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.6K

科学领域:

  • 生物识别信息 生物识别信息
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 个人身份验证系统需要强大而高效的方法.
  • 心电图 (ECG) 信号含有独特的生理模式,适合生物识别.
  • 现有的身份验证方法可能会面临准确性,可扩展性或实时处理方面的挑战.

研究的目的:

  • 提出和评估一种新的1D-CNN框架,用于使用单个心电图心跳的个人身份验证.
  • 评估单个心跳段的充分性,以创建一个强大的生物识别系统.
  • 调查SMOTE在处理基于ECG的身份验证中的数据不平衡方面的有效性.

主要方法:

  • 为个人身份验证开发了一个1D-CNN框架.
  • 从心电图信号中生成单个心跳样本,使用R-to-R细分,长度值和插值.
  • 合成少数群体过量采样技术 (SMOTE) 应用于解决样本分布不平衡问题.
  • 该框架在四个公开的ECG数据库 (NSRDB,MIT-ARR,ECG-ID,MIMIC-III) 上进行了验证.

主要成果:

  • 在个别的NSRDB和MIT-ARR数据库上,1D-CNN框架获得了完美的分数 (100%准确度,精度,灵敏度,F1分数).
  • 在具有较大人口和多种条件的混合数据集上,性能仍然很高 (>99.6%).
  • 该模型在小型和大型学科群体中展示了出色的可扩展性.

结论:

  • 一个单一的心跳段对于一个强大的基于1D-CNN的个人身份验证系统来说就足够了.
  • 拟议的框架提供了高精度和可扩展性,适合安全应用.
  • 未来的工作应该探索多式联络生物识别和实时实施,以获得更广泛的适用性.