基于表面肌电信号的卷积盲源分离用于呼吸诊断和医疗通气控制
Convolutive Blind Source Separation on Surface EMG Signals for Respiratory Diagnostics and Medical Ventilation Control
AI总结：
本文提出利用卷积盲源分离技术对表面肌电信号进行预处理，以解决区分吸气、呼气和心肌活动的问题，从而提升呼吸诊断和医疗通气控制的应用效果。
摘要：
肌电图（EMG）是评估肌肉活动的重要工具，因此也是呼吸支持诊断和控制的重要测量手段。本文提出卷积盲源分离（BSS）作为一种有效的预处理方法，用于处理人类呼吸肌肉的表面肌电信号（sEMG）。具体而言，本文针对区分吸气、呼气和心肌活动的问题进行了研究，该问题目前是sEMG在自适应通气控制中应用的主要障碍。研究表明，使用所研究的宽带算法，可以实现这些成分的清晰分离。该算法基于一个通用的BSS框架，利用多种统计信号特性。除了四通道FIR结构外，对解混系统没有其他限制性假设。
英文摘要：
The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an effective tool to pre-process surface electromyogram (sEMG) data of the human respiratory muscles. Specifically, the problem of discriminating between inspiratory, expiratory and cardiac muscle activity is addressed, which currently poses a major obstacle for the clinical use of sEMG for adaptive ventilation control. It is shown that using the investigated broadband algorithm, a clear separation of these components can be achieved. The algorithm is based on a generic framework for BSS that utilizes multiple statistical signal characteristics. Apart from a four-channel FIR structure, there are no further restrictive assumptions on the demixing system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
