Study of automatic fetal diagnostic algorithms based on abdominal signals
Objective Electronic fetal monitoring is an important means of assessing the intrauterine status of the fetus by continuously monitoring changes in fetal heart rate and uterine contractions.However traditional manual diagnosis of electronic fetal monitoring has the limitations of high subjectivity and low consistency.Compared to the ultrasound Doppler method,the transabdominal electrical signal method permits longer monitoring and is less affected by individual differences,yet there is a lack of accurate automated fetal diagnostic algorithms based on abdominal signals in China.For this reason,this study proposes an innovative algorithm that aims to improve the accuracy and efficiency of fetal diagnosis and to provide domestic clinicians with an effective decision-support tool to enhance the quality of healthcare services.Methods This algorithm uses a stable and effective multitask deep learning network to analyze the fetal heart rate extracted from the abdominal electrical signals to obtain the fetal heart rate parameters(baseline,acceleration and deceleration starting and stopping times),and the electrohysterogram signals obtained by filtering the abdominal signals are used for the recognition of uterine contractions to obtain the parameters of uterine contractions(uterine contraction frequency and starting and stopping times).These above-extracted parameters are sorted out and combined with the expert consensus on the application of electronic fetal monitoring for the diagnosis of fetal monitoring results.Results By analyzing the 20-minute monitoring recordings of 89 cases of simultaneous use of abdominal dynamic fetal monitor and Doppler fetal monitor,the algorithm demonstrates a sensitivity of 76.09%and a positive predictive value of 97.22%for the identification of none-stress test reactivity type,and a sensitivity of 88.89%and a positive predictive value of 68.09%for the contraction stress test category I,and an overall accuracy of 77.53%.Conclusions The algorithm demonstrates a high degree of consistency in comparisons with physicians'diagnostic results,providing an innovative aid for improving the quality of clinical decision-making and healthcare delivery.
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