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

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

981
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
981
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

10
Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
10
Pulse rhythm01:30

Pulse rhythm

815
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
815

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

Correction: Rao et al. Ensemble Deep-Learning-Based Prognostic and Prediction for Recurrence of Sporadic Odontogenic Keratocysts on Hematoxylin and Eosin Stained Pathological Images of Incisional Biopsies. <i>J. Pers. Med.</i> 2022, <i>12</i>, 1220.

Journal of personalized medicine·2026
Same author

Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images.

Scientific reports·2026
Same author

Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.

Scientific reports·2026
Same author

Quantum-resistant hybrid encryption framework for secure and intelligent Vehicle-to-Vehicle communication using deep representation learning models.

Scientific reports·2026
Same author

TwinGuard-Sec: a federated blockchain-enabled AI framework for standardized security and privacy in cross-domain digital twin ecosystems over 6G.

Scientific reports·2026
Same author

Ethical considerations in the integration of artificial intelligence into education: a novel deep neural network framework for predicting transparency scores.

Scientific reports·2026

相关实验视频

Updated: Jul 13, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

使用农田生育算法与物联网环境中的混合深度学习模型进行自动化心律失常分类.

Ahmed S Almasoud1, Hanan Abdullah Mengash2, Majdy M Eltahir3

  • 1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括

本研究介绍了在物联网平台内使用混合深度学习和农田生育算法的自动心律失常分类系统. 这种方法增强了远程患者监测和早期发现异常心律的方法.

关键词:
电动心电图信号 电动心电图信号物联网的物联网,就是物联网.心律失常的分类是心律失常的分类.深度学习是一种深度学习.远程监控 远程监控 远程监控

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

3.7K

相关实验视频

Last Updated: Jul 13, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

3.7K

科学领域:

  • 生物医学工程 生物医学工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 能够实现集中式的医疗记录管理.
  • 电心电图 (ECG) 对于诊断心脏病至关重要.
  • 手动ECG分析心律失常是耗时且具有挑战性的.

研究的目的:

  • 在物联网平台上展示一个使用混合深度学习和农田生育算法 (AAC-FFAHDL) 的自动化心律失常分类系统.
  • 提高心律失常检测和分类的效率和准确性.
  • 为了实现远程患者护理和持续监测心脏病状况.

主要方法:

  • 数据预处理以标准化心电图信号.
  • 混合深度学习 (HDL) 用于心律失常的检测和分类.
  • 农田生育算法 (FFA) 用于对HDL模型进行超参数调整.
  • 使用基准ECG数据库进行验证.

主要成果:

  • AAC-FFAHDL系统在自动心律失常分类方面表现出了有希望的表现.
  • 与其他模型相比,在各种评估指标上取得了优异的结果.
  • 通过超参数调整的深度学习模型有效诊断心律失常.

结论:

  • 拟议的AAC-FFAHDL方法为物联网环境中的自动心律失常分类提供了有效的解决方案.
  • 突出了将先进的人工智能算法与物联网集成的潜力,以改善心脏护理.
  • 支持远程患者监测和早期检测心律异常.