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

Classification of Illness01:17

Classification of Illness

9.4K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
9.4K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Classification of Systems-II01:31

Classification of Systems-II

651
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
651

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

MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.

Journal of imaging·2026
Same author

DiagPat: An Explainable Language Detection Model Using EEG Signals.

Sensors (Basel, Switzerland)·2026
Same author

A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.

BMC medicine·2026
Same author

Different pattern: a new EEG-based method for mental performance detection.

BMC medical informatics and decision making·2026
Same author

PyramidPat explainable feature engineering for multiclass electroencephalography psychiatric disorders: Explainable feature engineering and classification.

Psychiatry research·2026
Same author

A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.

Scientific reports·2026

相关实验视频

Updated: May 1, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.1K

使用顺序过渡模式特征工程技术与生理信号开发的新型准确分类系统.

Mehmet Ali Gelen1, Prabal Datta Barua2, Irem Tasci3

  • 1Department of Cardiology, Elazig Fethi Sekin City Hospital, Elazig, Turkey.

Scientific reports
|May 1, 2025
PubMed
概括

本研究介绍了一种可解释特征工程 (XFE) 模型,使用顺序过渡模式 (OTPat) 进行精确的EEG和ECG信号分类. 新的框架达到95%以上的准确性,提供可解释的连接组图.

关键词:
生物医学信号分类的分类这种心脏病是心脏病.定向的垂直垂体.可以解释的特征工程.在OTPat上的OTPat.这就是TkNNNN的意思.

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
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

1.5K

相关实验视频

Last Updated: May 1, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
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

1.5K

科学领域:

  • 生物医学信号处理
  • 医疗保健中的机器学习
  • 可解释的人工智能

背景情况:

  • 精确的脑电图 (EEG) 和心电图 (ECG) 信号的分类对于诊断神经和心脏疾病至关重要.
  • 现有的方法往往缺乏解释性,阻碍了临床采用.
  • 特性工程在提高复杂生物医学信号的分类性能方面发挥着至关重要的作用.

研究的目的:

  • 开发一种新的可解释特征工程 (XFE) 框架,用于高精度的EEG和ECG信号分类.
  • 提高机器学习模型在生物医学信号分析中的可解释性.
  • 验证关于不同EEG和ECG数据集的拟议框架.

主要方法:

  • 利用顺序过渡模式 (OTPat) 功能提取器来捕获信号中的空间和时间模式.
  • 使用累积加权代邻域组件分析 (CWINCA) 进行特征选择.
  • 使用t-算法k-近邻 (tkNN) 分类器对特征进行分类.
  • 通过使用Directed Lobish (DLob) 和 Cardioish符号语言生成可解释的结果,用于连接组图.

主要成果:

  • 在多个EEG和ECG数据集上实现了超过95%的分类准确性.
  • 在一个具有挑战性的8类EEG文物数据集上显示了86.07%的准确性.
  • 基于OTPat的XFE模型提供了清晰,可解释的连接组图,用于结果可视化.

结论:

  • 拟议的基于OTPat的XFE模型为生物医学信号分类提供了强大的,高度准确的解决方案.
  • 该框架的可解释性,通过符号语言,增强了对临床应用的信任和理解.
  • 这种方法在促进神经学和心脏病学诊断能力方面具有重大潜力.