Stimulus artifact removal using a software-based two-stage peak detection algorithm.
The analysis of stimulus evoked neuromuscular potentials or m-waves is a useful technique for improved feedback control in functional electrical stimulation systems. Usually, however, these signals are contaminated by stimulus artifact. A novel software technique, which uses a two-stage peak detection algorithm, has been developed to remove the unwanted artifact from the recorded signal. The advantage of the technique is that it can be used on all stimulation artifact-contaminated electroneurophysiologic data provided that the artifact and the biopotential are non-overlapping. The technique does not require any estimation of the stimulus artifact shape or duration. With the developed technique, it is not necessary to record a pure artifact signal for template estimation, a process that can increase the complexity of experimentation. The technique also does not require the recording of any external hardware synchronisation pulses. The method avoids the use of analogue or digital filtering techniques, which endeavour to remove certain high frequency components of the artifact signal, but invariably have difficulty, resulting in the removal of frequencies in the same spectrum as the m-wave. With the new technique the signal is sampled at a high frequency to ensure optimum fidelity. Instrumentation saturation effects due to the artifact can be avoided with careful electrode placement. The technique was fully tested with a wide variety of electrical stimulation parameters (frequency and pulse width) applied to the common peroneal nerve to elicit contraction in the tibialis anterior. The program was also developed to allow batch processing of multiple files, using closed loop feedback correction. The two-stage peak detection artifact removal algorithm is demonstrated as an efficient post-processing technique for acquiring artifact free m-waves.
O'Keeffe
D T
DT
Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland. derek.okeeffe@ul.ie
Lyons
G M
GM
Donnelly
A E
AE
Byrne
C A
CA
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D016477:Artifacts; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D009469:Neuromuscular Junction / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012984:Software / Q000592:standards*; D009435:Synaptic Transmission / Q000502:physiology