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

Heart Sounds01:15

Heart Sounds

3.1K
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)...
3.1K
Classification of Signals01:30

Classification of Signals

1.3K
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...
1.3K
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

293
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
293
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

329
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
329
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.5K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
11.5K

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

NIR-Programmable Stealth 2D Black Phosphorus Nanobiointerfaces for Deep Tumor Penetration and Photoimmunotherapy.

ACS nano·2026
Same author

A wearable paper-based SGR/MCC microneedle array sensor for continuous glucose monitoring.

Microsystems & nanoengineering·2026
Same author

Simultaneous multi-band multi-spectral imaging using multi-band RF excitation for accelerated metal artifact reduction in MRI-guided interventions.

Medical physics·2026
Same author

An integrated electrochemical microfluidic chip for simultaneous detection of uric acid and interleukin-6.

Mikrochimica acta·2026
Same author

Moral inconsistency is based on the vmPFC's insufficient representation across tasks and connectedness.

Cell reports·2026
Same author

Harmonic-mapping-based design of gradient coils on irregular MRI surfaces.

Medical physics·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
Same journal

A Low-Cost Wearable TI-TACS Stimulator With Bipolar Quadratic-Boost Converter for Current Stimulation Validation in the Rat Brain.

IEEE transactions on bio-medical engineering·2026
Same journal

EMG-Based Gait Estimation Using Koopman-Inspired Method.

IEEE transactions on bio-medical engineering·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

399

一个深度学习模型用于心脏声音分类,融合时间频率特征.

Nuo Liu, Xiayu Chen, Yueyi Yu

    IEEE transactions on bio-medical engineering
    |December 10, 2025
    PubMed
    概括
    此摘要是机器生成的。

    一个新的双分支深度学习模型有效地融合了时间和频率域特征,以改进心电图 (PCG) 分类. 这种先进的方法提高了心血管疾病诊断的准确性和稳定性.

    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

    2.9K
    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    883

    相关实验视频

    Last Updated: Jan 9, 2026

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    399
    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

    2.9K
    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    883

    科学领域:

    • 生物医学工程 生物医学工程
    • 医疗保健中的人工智能
    • 心脏病学 心脏病学

    背景情况:

    • 心血管疾病 (CVD) 构成了全球健康的重大风险.
    • 准确的心电图 (PCG) 信号分类对于早期心血管疾病诊断至关重要.
    • 现有的模型经常单独分析时间或频率域,从而限制了诊断潜力.

    研究的目的:

    • 开发一个自动PCG信号分类的先进模型.
    • 通过整合时间和频率特征来克服单域分析的局限性.
    • 提高分类准确性和稳定性,以提高心血管疾病诊断.

    主要方法:

    • 提出了一个新的端到端双分支深度学习架构.
    • 时间域分支:1D CNN与变压器块用于动态和依赖关系.
    • 频域分支:ResNet 在Mel-光谱图上的频域分支.
    • 使用双向交叉注意力融合模块进行特征交互.
    • 员工转移学习,以在各种数据集上实现强大的性能.

    主要成果:

    • 在多个公共数据集中实现了最先进的 (SOTA) 性能.
    • 在2016年PhysioNet挑战数据集上获得了98.86%的准确性和97.19%的F1分数.
    • 在心声分类方面显著优于现有的基线方法.

    结论:

    • 双分支融合模型为心脏声音分类提供了一个卓越的框架.
    • 证明了对心血管疾病高度准确的自动诊断工具的潜力.
    • 支持心血管医学中提升早期检测和改善临床结果.