Adaptive cancellation of the respiratory artifact in surface recording of small intestinal electrical activity.
In the human small intestine there is omnipresent electrical activity with a frequency of 0.15-0.2 Hz. The electrical activity of the small intestine can be measured by surface electrodes placed on the abdominal skin. The most annoying problem in the surface electrical recording is the respiratory artifact which is not discernible from the small intestinal signal. The frequency of the respiration is about 0.2-0.4 Hz, which is very close to that of small intestinal activity, making the use of the conventional bandpass filtering impractical. In this paper a selective frequency domain adaptive filter was proposed for the cancellation of the respiratory artifact. The basic principle of the selective frequency domain adaptive filter is that only selected filter weights are adapted based on the frequency characteristics of the respiratory artifact. Therefore, a substantial reduction of computation is achieved. A series of computer simulations was conducted for the optimization of the system parameters and for the investigation of the system performance. It was demonstrated in this paper that the selective frequency domain adaptive filter is as effective as, but more efficient than, the conventional frequency domain adaptive filter. The adaptive system for the cancellation of the respiratory artifact based on the selective frequency domain adaptive filter is very efficient in computation, has a fast convergence (about 100 adaptations), substantial reduction of the respiratory artifact and little effect (or distortion) on the small intestinal electrical signal.
Chen
J D
JD
Health Science Center, University of Virginia, Charlottesville 22908.
Lin
Z
Z
D003198:Computer Simulation; D004566:Electrodes; D004576:Electromyography; D004594:Electrophysiology; D006801:Humans; D007421:Intestine, Small / Q000502:physiology*; D008954:Models, Biological; D012016:Reference Values; D012119:Respiration / Q000502:physiology; D012815:Signal Processing, Computer-Assisted