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

3.2K
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)...
3.2K
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
Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

1.7K
Cardiac auscultation is a clinical skill used to assess heart function and detect abnormalities. It involves listening to heart sounds at specific anatomical locations through a stethoscope.
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.
1.7K
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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

[Application of multi-scale spatiotemporal networks in physiological signal and facial action unit measurement].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2025
Same author

Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders.

International journal of molecular sciences·2024
Same author

Reversing Resistance of Cancer Stem Cells and Enhancing Photodynamic Therapy Based on Hyaluronic Acid Nanomicelles for Preventing Cancer Recurrence and Metastasis.

Advanced healthcare materials·2023
Same author

Cross-Talking Pathways of Rapidly Accelerated Fibrosarcoma-1 (RAF-1) in Alzheimer's Disease.

Molecular neurobiology·2023
Same author

Pharmacokinetics and tissue distribution of four major bioactive components of <i>Cynanchum auriculatum</i> extract: a UPLC-MS/MS study in normal and functional dyspepsia rats.

Frontiers in pharmacology·2023
Same author

Treatment of Severe Pulmonary Regurgitation in Enlarged Native Right Ventricular Outflow Tracts: Transcatheter Pulmonary Valve Replacement with Three-Dimensional Printing Guidance.

Bioengineering (Basel, Switzerland)·2023

相关实验视频

Updated: Jan 13, 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

423

[基于改进的Mel频 cepstrum系数特征提取和深度变压器的心声分类算法的研究]

Xin Meng1, Sunjie Zhang1

  • 1School of Opto-Electronic Information & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, P. R. China.

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
|October 28, 2025
PubMed
概括

这项研究引入了一种先进的心声分类方法,使用改进的Mel-frequency cepstral系数 (MFCC) 和深度学习模型. 这种新的方法有效地分类连续的心脏声音,改善心血管疾病的检测.

关键词:
深度变压器 深度变压器焦点损失 焦点损失全球平均汇聚的平均值.心脏声音分类心脏声音分类修改的Mel频率塞普斯特姆系数

更多相关视频

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

914
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.7K

相关实验视频

Last Updated: Jan 13, 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

423
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

914
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.7K

科学领域:

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

背景情况:

  • 传统的心声分析方法与动态特征和数据不平衡作斗争.
  • 现有的浅层分类器经常无法有效捕捉复杂的心脏声音模式.
  • 准确的心声分类对于早期发现心血管疾病至关重要.

研究的目的:

  • 开发一种新的心声分类方法,解决现有方法的局限性.
  • 通过心脏声音分析提高心血管疾病检测的准确性和稳定性.
  • 为了实现连续心脏声信号的高效分类,用于实际应用.

主要方法:

  • 利用改进的Mel频率塞普斯特拉系数 (MFCC) 来从连续的心声信号中提取功能.
  • 采用卷积神经网络 (CNN) 进行初始特征学习,其次是全球平均聚合 (GAP).
  • 集成了一个深度变压器模型,用于先进的功能融合和分类,使用焦点损失来管理数据不平衡.

主要成果:

  • 拟议的方法在二进制和多类心声分类任务中都表现出有效的性能.
  • 在公共数据集上的实验证实了该模型捕获动态信号特征的能力.
  • 该方法成功地缓解了与数据不平衡和模型过拟合有关的问题.

结论:

  • 这种新的心声分类方法为心血管疾病检测提供了强大而高效的解决方案.
  • 这种方法为未来的心声分析和疾病分类研究提供了宝贵的参考.
  • 开发的方法支持可穿戴设备和家庭心脏健康监测系统的进步.