一个基于U-Net的局部卷积时域分离模型,用于实时识别来自表面电肌图信号的电机单元
1School of Information Science and Technology, Dalian Maritime University, Linghai Road 1, Dalian, Liaoning Province 116026, China.
概括
本研究介绍了一种基于U-Net的模型,用于实时高密度表面电肌图 (HD-sEMG) 分解,有效地识别动力单元 (MU). 该模型实现了高精度和低延迟,优于神经相互作用应用的传统方法.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 高密度表面电肌图 (HD-sEMG) 信号分解对于理解神经肌肉活动至关重要.
- 由于信号复杂性和噪声,从HD-sEMG中准确和实时识别电机单元 (MU) 是一个挑战.
- 现有的方法往往需要大量的预处理或遭受高计算成本.
研究的目的:
- 为实时HD-sEMG分解提出一种基于U-Net的部分卷积时间域模型.
- 在没有预处理的情况下,直接从高清sEMG信号中有效地识别动力单元 (MU).
- 与现有方法相比,评估模型的准确性,延迟和通用性.
主要方法:
- 开发基于U-Net的网络,采用部分卷积分离块.
- 使用带有内置脉冲列车 (IPT) 标记的HD-sEMG信号来训练模型.
- 在不同的条件下进行评估,包括不同的步骤大小,噪音水平和使用移动时间窗口的模型架构.
主要成果:
- 在模拟的HD-sEMG信号上获得了>94%的准确性,在实验的HD-sEMG信号上获得了85%的准确性.
- 与基于卷积神经网络 (CNN) 和时间卷积网络 (TCN) 的模型相比,确定了更多的MU.
- 显示了64毫秒的平均延迟和显著的效率提高比传统的方法,如CGKC,与可比的准确性.
结论:
- 基于U-Net的部分卷积模型为HD-sEMG信号的盲源分离 (BSS) 提供了一个高效和准确的框架.
- 该模型的实时能力和高性能扩大了HD-sEMG在神经相互作用中的潜在应用.
- 拟议的方法在不同的信号噪声比率 (SNR) 和滑动窗参数中证明了稳定性和通用性.
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