title: Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control.
abstract: 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.
authors.forename: Herbert
authors.initials: H
authors.lastname: Buchner
authors.forename: Eike
authors.initials: E
authors.lastname: Petersen
authors.forename: Marcus
authors.initials: M
authors.lastname: Eger
authors.forename: Philipp
authors.initials: P
authors.lastname: Rostalski
mesh_terms: D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D006801:Humans; D012121:Respiration, Artificial; D012132:Respiratory Muscles / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted