Details

Autor(en) / Beteiligte

• Buchner, Herbert
• Petersen, Eike
• Eger, Marcus
• Rostalski, Philipp

Titel

Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control

Ist Teil von

• 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, Vol.2016, p.3626-3629

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2016

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.