Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

An adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potent...

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Veröffentlicht in:IEEE transactions on biomedical engineering Jg. 39; H. 10; S. 1032 - 1044
Hauptverfasser: Laguna, P., Jane, R., Meste, O., Poon, P.W., Caminal, P., Rix, H., Thakor, N.V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.10.1992
Institute of Electrical and Electronics Engineers
Schlagworte:
Adaptive filters
Additive noise
Algorithms
Bioelectric phenomena
Bioengineering
Biological and medical sciences
Colored noise
Computer Science
Electric potential
Electric Stimulation
Electrocardiography
Electrocardiography - methods
Electrodiagnosis. Electric activity recording
Engineering Sciences
Evoked Potentials, Somatosensory
Humans
Investigative techniques, diagnostic techniques (general aspects)
Least squares approximation
Life Sciences
Mathematics
Medical sciences
Miscellaneous. Technology
Nonlinear filters
Signal and Image processing
Signal processing
Signal processing algorithms
Signal Transduction
Electrical signal
High resolution
Retarded potential
Noise reduction
Electrophysiology
Adaptive filter
Experimental study
Algorithm
Electrodiagnosis
Evoked potential
Electrocardiography
Signal processing
Technique
Biomedical engineering
ISSN:0018-9294
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Zusammenfassung:An adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.< >
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ISSN:0018-9294
DOI:10.1109/10.161335