摘要Cardiotocography measures the human fetal heart rate and uterine activity using ultrasound. While it has been a mainstay in antepartum care since the 1960s, cardiotocograms consist of complex signals that have proven difficult for clinicians to interpret accurately and as such clinical inference is often difficult and unreliable. Previous attempts at codifying approaches to analyzing the features within these signals have failed to demonstrate reliability or gain sufficient traction. Since the early 1990s, the Dawes-Redman system of automated computer analysis of cardiotocography signals has enabled robust analysis of cardiotocographic signal features, employing empirically-derived criteria for assessing fetal wellbeing in the antepartum. Over the past 30 years, the Dawes-Redman system has been iteratively updated, now incorporating analyses from over 100,000 pregnancies. In this review, we examine the history of cardiotocography, signal processing methodologies and feature identification, the development of the Dawes-Redman system, and its clinical applications.

abstractsCardiotocography measures the human fetal heart rate and uterine activity using ultrasound. While it has been a mainstay in antepartum care since the 1960s, cardiotocograms consist of complex signals that have proven difficult for clinicians to interpret accurately and as such clinical inference is often difficult and unreliable. Previous attempts at codifying approaches to analyzing the features within these signals have failed to demonstrate reliability or gain sufficient traction. Since the early 1990s, the Dawes-Redman system of automated computer analysis of cardiotocography signals has enabled robust analysis of cardiotocographic signal features, employing empirically-derived criteria for assessing fetal wellbeing in the antepartum. Over the past 30 years, the Dawes-Redman system has been iteratively updated, now incorporating analyses from over 100,000 pregnancies. In this review, we examine the history of cardiotocography, signal processing methodologies and feature identification, the development of the Dawes-Redman system, and its clinical applications.