Scholarlink

<p>To compensate for the shortcomings of existing diagnostic methods in assessing the severity of peripheral arterial disease (PAD), this study proposed a novel non-invasive protocol for both diagnosing PAD and stratifying its severity. Surface electromyography (sEMG) and mechanomyography (MMG) signals from the gastrocnemius muscle were collected from subjects. The continuous wavelet transform was used to generate the Morlet scalogram of the processed sEMG. Subsequently, conventional feature extraction techniques were employed to procure time domain, frequency domain, and nonlinear features from the sEMG and MMG. A multimodal cascade fusion network was developed to integrate information from scalograms and multidomain features, enabling the detection and severity assessment of lower extremity PAD. The proposed method was subjected to a fourfold cross-validation experiment in 40 lower extremities. The mean sensitivity, specificity, accuracy, and F1 score were 92.36%, 96.23%, 95.00%, and 94.17%, respectively. The clinical evaluation of the method was performed on 18 lower extremities, with 17 lower extremities accurately classified. The achieved performance demonstrates the feasibility of assessing lower extremity PAD severity using gastrocnemius sEMG and MMG signals, providing a promising tool for routine PAD screening and aiding clinicians in informed decision-making.</p> Graphical abstract <p>A Non-invasive Method for Assessing the Severity of Peripheral Arterial Disease (PAD): Pathological Changes in the gastrocnemius muscle of Lower Extremity PAD Patients. Extraction of Morlet Scalograms and Multidomain (MD) Features from Surface Electromyography (sEMG) and Mechanomyography (MMG) Signals, Deep Scalogram Feature Extraction with CNN, and Fusion for PAD Severity Stratification Using KAN Networks.</p> <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of the severity of lower extremity peripheral arterial disease based on gastrocnemius surface electromyography and mechanomyography signals

  • 期刊论文

  • 发表日期：

    2026年3月31日

查看全文
  • Wenxuan Sun,
  • Yanbin Guo,
  • Shuwei Lin,
  • Guoping Wang,
  • Youpeng Zhu,
  • Yao Zhang,
  • Ye Du,
  • Zibo Feng

摘要

To compensate for the shortcomings of existing diagnostic methods in assessing the severity of peripheral arterial disease (PAD), this study proposed a novel non-invasive protocol for both diagnosing PAD and stratifying its severity. Surface electromyography (sEMG) and mechanomyography (MMG) signals from the gastrocnemius muscle were collected from subjects. The continuous wavelet transform was used to generate the Morlet scalogram of the processed sEMG. Subsequently, conventional feature extraction techniques were employed to procure time domain, frequency domain, and nonlinear features from the sEMG and MMG. A multimodal cascade fusion network was developed to integrate information from scalograms and multidomain features, enabling the detection and severity assessment of lower extremity PAD. The proposed method was subjected to a fourfold cross-validation experiment in 40 lower extremities. The mean sensitivity, specificity, accuracy, and F1 score were 92.36%, 96.23%, 95.00%, and 94.17%, respectively. The clinical evaluation of the method was performed on 18 lower extremities, with 17 lower extremities accurately classified. The achieved performance demonstrates the feasibility of assessing lower extremity PAD severity using gastrocnemius sEMG and MMG signals, providing a promising tool for routine PAD screening and aiding clinicians in informed decision-making.

Graphical abstract

A Non-invasive Method for Assessing the Severity of Peripheral Arterial Disease (PAD): Pathological Changes in the gastrocnemius muscle of Lower Extremity PAD Patients. Extraction of Morlet Scalograms and Multidomain (MD) Features from Surface Electromyography (sEMG) and Mechanomyography (MMG) Signals, Deep Scalogram Feature Extraction with CNN, and Fusion for PAD Severity Stratification Using KAN Networks.