2023
DOI: 10.1016/j.compbiomed.2023.106673
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A novel framework for the removal of pacing artifacts from bio-electrical recordings

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Cited by 6 publications
(1 citation statement)
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“…The large pacing artifacts superimposed on the slow wave recordings were detected using a Hampel outlier detection filter (number of neighbors = 15; number of standard deviations = 25) and removed using an autoregressive model 39 . The model parameters of the autoregressive model were defined using data accounting for a predefined window length of 0.5 s from both sides of each artifact segment.…”
Section: Methodsmentioning
confidence: 99%
“…The large pacing artifacts superimposed on the slow wave recordings were detected using a Hampel outlier detection filter (number of neighbors = 15; number of standard deviations = 25) and removed using an autoregressive model 39 . The model parameters of the autoregressive model were defined using data accounting for a predefined window length of 0.5 s from both sides of each artifact segment.…”
Section: Methodsmentioning
confidence: 99%