Cardiac pacing is a clinical therapy widely used for treating irregular heart rhythms. Equivalent techniques for the treatment of gastric functional motility disorders hold great potential. Accurate analysis of pacing studies is often hindered by the stimulus artifacts which are superimposed on the recorded signals. This paper presents a semi-automated artifact detection method using a Hampel filter accompanied by 2 separate artifact reconstruction methods: (i) an auto-regressive model, and (ii) weighted mean to estimate the underlying signal. The developed framework was validated on synthetic and experimental signals containing large periodic pacing artifacts alongside evoked bioelectrical events. The performance of the proposed algorithms was quantified for gastric and cardiac pacing data collected in vivo. A lower mean RMS difference was achieved by the artifact segment reconstructed using the auto-regression ([Formula: see text]), method compared to the weighted mean ([Formula: see text]) method. Therefore, a more accurate artifact reconstruction was provided by the auto-regression approach. Clinical Relevance- The ability to efficiently and accurately isolate evoked bioelectrical events by eliminating large artifacts is a critical advancement for the analysis of paced recordings. The developed framework allows more efficient analysis of preclinical pacing data and thereby contributes to the advancement of pacing as a clinical therapy.