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<p>Calf Neuromuscular Electrical Stimulation (NMES) may reduce the risk of Venous Thromboembolism (VTE) by increasing venous blood flow. The study compared motor point-placed traditional gel electrodes (MPE) with secured Transverse Textile Electrodes (TTE) implanted into socks to measure peak venous velocity (PVV) and pain during calf neuromuscular electrical stimulation (calf-NMES). In this paper, Detecting Venous Blood in Calf Muscles using Optimized Granger Causality-Inspired Graph Neural Network (VBF-CMNMES-GCGNN) is proposed. Here, the input data are gathered from 16 healthy participants between 18 and 60. Afterwards, the data are given into Multi-Window Savitzky-Golay Filter (MWSF) for pre-processing to perform data cleaning, removing unwanted data and replacing the missing values. The proposed VBF-CMNMES-GCGNN strategy is implemented in Python. The performance metrics, like F1-score, accuracy, Mean Absolute Error (MAE) and AUC (Area under the Curve) are examined. The proposed VBF-CMNMES-GCGNN attains 22.98%, 25.28% and 17.19% higher accuracy and 22.98%, 17.28% and 27.89% lower MAE when compared with the existing techniques like Wearable neuromuscular electrical stimulation on quadriceps muscle can improve venous flow (Q-NMES-IVF), Measurement of arterial occlusion pressure utilizing straight and curved blood flow restriction cuffs (AOP-SC-BFRC) and Effects on hemodynamic enhancement with distress of new textile electrode incorporated in a sock during calf neuromuscular electrical stimulation (TTE-CNMSE) respectively.</p>

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Detecting Venous Blood in Calf Muscles Using Optimized Granger Causality-Inspired Graph Neural Network

  • 期刊论文

  • 发表日期：

    2025年12月28日

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  • R. Naveeth Kumar,
  • B. Buvaneswari,
  • S. Divya,
  • M. Jeyamurugan,
  • M. R. Mano Jemila

摘要

Calf Neuromuscular Electrical Stimulation (NMES) may reduce the risk of Venous Thromboembolism (VTE) by increasing venous blood flow. The study compared motor point-placed traditional gel electrodes (MPE) with secured Transverse Textile Electrodes (TTE) implanted into socks to measure peak venous velocity (PVV) and pain during calf neuromuscular electrical stimulation (calf-NMES). In this paper, Detecting Venous Blood in Calf Muscles using Optimized Granger Causality-Inspired Graph Neural Network (VBF-CMNMES-GCGNN) is proposed. Here, the input data are gathered from 16 healthy participants between 18 and 60. Afterwards, the data are given into Multi-Window Savitzky-Golay Filter (MWSF) for pre-processing to perform data cleaning, removing unwanted data and replacing the missing values. The proposed VBF-CMNMES-GCGNN strategy is implemented in Python. The performance metrics, like F1-score, accuracy, Mean Absolute Error (MAE) and AUC (Area under the Curve) are examined. The proposed VBF-CMNMES-GCGNN attains 22.98%, 25.28% and 17.19% higher accuracy and 22.98%, 17.28% and 27.89% lower MAE when compared with the existing techniques like Wearable neuromuscular electrical stimulation on quadriceps muscle can improve venous flow (Q-NMES-IVF), Measurement of arterial occlusion pressure utilizing straight and curved blood flow restriction cuffs (AOP-SC-BFRC) and Effects on hemodynamic enhancement with distress of new textile electrode incorporated in a sock during calf neuromuscular electrical stimulation (TTE-CNMSE) respectively.