Jove
Visualize
联系我们

相关概念视频

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

Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.

Bioengineering (Basel, Switzerland)·2026
Same author

Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.

Diagnostics (Basel, Switzerland)·2025
Same author

Development and validation of a multi-modal contactless sensing system for surgical risk analysis in a real-world environment.

PLOS digital health·2025
Same author

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.

Sensors (Basel, Switzerland)·2023
Same author

Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal.

Sensors (Basel, Switzerland)·2022
Same author

Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章
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 1, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

872

通过可解释的人工智能解释中风损伤的肌电图案.

Iqram Hussain1, Rafsan Jany2

  • 1Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种可解释的机器学习模型,使用电肌图 (EMG) 来检测与中风相关的步行障碍. 该模型准确地区分中风患者和健康个体,为康复提供了一个新的工具.

关键词:
座 座 座 座在 LIME 时代,这就是 SHAP SHAP 的意思.电动肌谱学 电动肌谱学可以解释的人工智能AI一次性中风中风中风中风中风

更多相关视频

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

1.2K

相关实验视频

Last Updated: Jul 1, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

872
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

1.2K

科学领域:

  • 生物医学工程 生物医学工程
  • 神经学 神经学
  • 机器学习 机器学习

背景情况:

  • 缺血性中风往往导致步行障碍,影响患者的康复和生活质量.
  • 电肌图 (EMG) 为神经肌肉功能提供了有价值的见解,可能作为中风诱导的步态问题的诊断标记.
  • 当前的诊断方法可能无法完全捕捉影响中风后步态的微妙神经肌肉变化.

研究的目的:

  • 开发一种可解释的机器学习 (ML) 框架,使用EMG信号来区分中风患者和健康个体.
  • 通过可解释的人工智能 (XAI) 技术,识别关键的EMG特征,表明与中风相关的步行障碍.
  • 建立一个客观的工具来预测和管理中风后的步行功能障碍.

主要方法:

  • 收集了48名中风患者和75名健康成年人的EMG数据.
  • 使用可穿戴传感器在双腿骨和两个下肢的侧面胃角肌肉.
  • 使用增强ML模型 (例如GBoost) 和XAI技术 (SHAP,LIME,Anchors) 来进行分类和解释.

主要成果:

  • 该GBoost模型实现了高分类性能,在训练组中AUROC为0.94,在测试组中为0.92 (准确率为85.26%).
  • 在XAI分析中,确定了右双腿骨和侧腹肌肌的特定EMG光谱特征,这些特征对于区分中风患者至关重要.
  • 可解释模型有效地突出了与中风相关的行走障碍相关的神经肌肉变化.

结论:

  • 一个可解释的基于EMG的ML模型可以准确地预测中风相关的步行障碍.
  • 这种方法为中风后的早期检测和个性化康复策略提供了一个有希望的,客观的工具.
  • 已识别的EMG生物标志物可以显著帮助控制中风幸存者的步行功能障碍.