Background　and　Objective:　Preterm　delivery　is　an　important　factor　in　the　disease　burden　of　the　newborn　and　infants　worldwide.　Electrohysterography　(EHG)　has　become　a　promising　technique　for　predicting　this　condition,　thanks　to　its　high　degree　of　sensitivity.　Despite　the　technological　progress　made　in　predicting　preterm　labor,　its　use　in　clinical　practice　is　still　limited,　one　of　the　main　barriers　being　the　lack　of　tools　for　automatic　signal　processing　without　expert　supervision,　i.e.　automatic　screening　of　motion　and　respiratory　artifacts　in　EHG　records.　Our　main　objective　was　thus　to　design　and　validate　an　automatic　system　of　segmenting　and　screening　the　physiological　segments　of　uterine　origin　in　EHG　records　for　robust　characterization　of　uterine　myoelectric　activity,　predicting　preterm　labor　and　help　to　promote　the　transferability　of　the　EHG　technique　to　clinical　practice.　Methods:　For　this,　we　combined　300　EHG　recordings　from　the　TPEHG　DS　database　and　69　EHG　recordings　from　our　own　database　(Ci2B-La　Fe)　of　women　with　singleton　gestations.　This　dataset　was　used　to　train　and　evaluate　U-Net,　U-Net++,　and　U-Net　3+　for　semantic　segmentation　of　the　physiological　and　artifacted　segments　of　EHG　signals.　The　model＇s　predictions　were　then　fine-tuned　by　post-processing.　Results:　U-Net　3+　outperformed　the　other　models,　achieving　an　area　under　the　ROC　curve　of　91.4　%　and　an　average　precision　of　96.4　%　in　detecting　physiological　activity.　Thresholds　from　0.6　to　0.8　achieved　precision　from　93.7　%　to　97.4　%　and　specificity　from　81.7　%　to　94.5　%,　detecting　high-quality　physiological　segments　while　maintaining　a　trade-off　between　recall　and　specificity.　Post-processing　improved　the　model＇s　adaptability　by　fine-tuning　both　the　physiological　and　corrupted　segments,　ensuring　accurate　artifact　detection　while　maintaining　physiological　segment　integrity　in　EHG　signals.　Conclusions:　As　automatic　segmentation　proved　to　be　as　effective　as　double-blind　manual　segmentation　in　predicting　preterm　labor,　this　automatic　segmentation　tool　fills　a　crucial　gap　in　the　existing　preterm　delivery　prediction　system　workflow　by　eliminating　the　need　for　double-blind　segmentation　by　experts　and　facilitates　the　practical　clinical　use　of　EHG.　This　work　potentially　contributes　to　the　early　detection　of　authentic　preterm　labor　women　and　will　allow　clinicians　to　design　individual　patient　strategies　for　maternal　health　surveillance　systems　and　predict　adverse　pregnancy　outcomes.