Jove
Visualize
联系我们

相关概念视频

Classification of Signals01:30

Classification of Signals

484
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
484
Heart Sounds01:15

Heart Sounds

2.0K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
2.0K

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

MYCN Regulates Cardiomyocyte Proliferation, Metabolism, and Regeneration in the Mammalian Heart.

Circulation·2025
Same author

Chronic Rhinosinusitis With Nasal Polyps Does Not Affect the Association Between the Nasal Provocation Test and Serum Allergen-Specific Immunoglobulin E Levels.

Journal of rhinology : official journal of the Korean Rhinologic Society·2024
Same author

A community effort to optimize sequence-based deep learning models of gene regulation.

Nature biotechnology·2024
Same author

Evaluation and optimization of sequence-based gene regulatory deep learning models.

bioRxiv : the preprint server for biology·2024
Same author

Factors associated with impaired psychophysical gustatory function.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery·2024
Same author

Mastering data visualization with Python: practical tips for researchers.

Journal of minimally invasive surgery·2023
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

相关实验视频

Updated: Jul 12, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

MCHeart:使用深度学习检测心脏噪声的多通道心脏信号处理方案.

Soyul Han1, Woongsun Jeon2, Wuming Gong3

  • 1Department of Applied Statistics, Chung-Ang University, Seoul 06974, Republic of Korea.

Biology
|October 27, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个AI模型,使用log-mel 2D谱图来预测异常的心脏声音. 通过结合心脏信号特征,ReLCNN模型提高了诊断准确性,增强了心脏声音分析.

关键词:
生物信号 生物信号深度学习是一种深度学习.功能提取 特性提取检测心脏声 检测心脏声轻微的 美国有线电视新闻网 (CNN)多重注意网络多重注意网络智能医疗保健是一个智能医疗保健.

更多相关视频

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.9K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K

相关实验视频

Last Updated: Jul 12, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.9K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 生物医学信号处理

背景情况:

  • 准确检测异常的心脏声音对于诊断心脏病至关重要.
  • 传统的方法往往依赖于对听数据的主观解释.
  • 开发客观,自动化的心脏声音分析方法是一个持续的挑战.

研究的目的:

  • 构建一个深度学习模型来预测异常的心脏声音.
  • 通过整合新的特征提取技术来增强心脏声分析.
  • 为了提高自动心脏声音分类的准确性.

主要方法:

  • 利用各种身体位置的各种听觉数据.
  • 将心脏信号转化为log-mel 2D谱图作为卷积神经网络 (CNN) 的输入.
  • 开发了一种基于多通道的心脏信号处理 (MCHeart) 方案和ReLCNN模型,其中包括残留块和多头注意力 (MHA) 机制.

主要成果:

  • 该ReLCNN模型,结合声特征和光滑功能,实现了83.6%的加权精度.
  • 与基线LCNN模型 (79.6%准确率) 相比,这代表了大约4%的性能改进.
  • 深度学习与专家衍生的心脏特征的整合显示出显著的潜力.

结论:

  • 拟议的ReLCNN模型有效地使用处理的听觉数据预测异常的心脏声音.
  • MCHeart 方案和 ReLCNN 架构为自动心脏诊断提供了一个有前途的进步.
  • 这种方法提高了心脏声音分析的准确性和客观性.