Background: Studies which use external tocography to explore the relationship between increased intrapartum uterine activity and foetal outcomes are feasible because the technology is safe and ubiquitous. However, periods of poor signal quality are common. We developed an algorithm which aims to calculate tocograph summary variables based on well-recorded contractions only, ignoring artefact and excluding sections deemed uninterpretable. The aim of this study was to test that algorithm's reliability.
Methods: Whole recordings from labours at ≥35 weeks of gestation were randomly selected without regard to quality. Contractions and rest intervals were measured by two humans independently, and by the algorithm using two sets of models; one based on a series of pre-defined thresholds, and another trained to imitate one of the human interpreters. The absolute agreement intraclass correlation coefficient (ICC) was calculated using a two-way random effects model.
Results: The training dataset included data from 106 tocographs. Of the tested algorithms, AdaBoost showed the highest initial cross-validated accuracy and proceeded to optimization. Forty tocographs were included in the validation set. The ICCs for the per tocograph mean contraction rates were; human B to human A: 0.940 (0.890-0.968), human A to initial models: 0.944 (0.898-0.970), human A to trained models 0.962 (0.927-0.980), human B to initial models: 0.930 (0.872-0.962), human B to trained models: 0.948 (0.903-0.972).
Conclusions: The algorithm described approximates interpretation of external tocography performed by trained humans. The performance of the AdaBoost trained models was marginally superior compared to the initial models.
Keywords: Agreement; Contraction duration; Contraction rate; Contractions; Foetal monitoring; Intrapartum; Labour; Machine learning; Reliability; Rest intervals; Tachysystole; Uterine activity.
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