Convolutive blind source separation of surface EMG measurements of the respiratory muscles.

Electromyography (EMG) has long been used for the assessment of muscle function and activity and has recently been applied to the control of medical ventilation. For this application, the EMG signal is usually recorded invasively by means of electrodes on a nasogastric tube which is placed inside the esophagus in order to minimize noise and crosstalk from other muscles. Replacing these invasive measurements with an EMG signal obtained non-invasively on the body surface is difficult and requires techniques for signal separation in order to reconstruct the contributions of the individual respiratory muscles. In the case of muscles with small cross-sectional areas, or with muscles at large distances from the recording site, solutions to this problem have been proposed previously. The respiratory muscles, however, are large and distributed widely over the upper body volume. In this article, we describe an algorithm for convolutive blind source separation (BSS) that performs well even for large, distributed muscles such as the respiratory muscles, while using only a small number of electrodes. The algorithm is derived as a special case of the TRINICON general framework for BSS. To provide evidence that it shows potential for separating inspiratory, expiratory, and cardiac activities in practical applications, a joint numerical simulation of EMG and ECG activities was performed, and separation success was evaluated in a variety of noise settings. The results are promising.

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

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.

Active listening room compensation for massive multichannel sound reproduction systems using wave-domain adaptive filtering.

The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis.