Key result
An algorithm using fetal heart rate variability analysis from phonocardiographic recordings and a multilayer perceptron neural network showed promising performance for predicting fetal wellbeing.
Why the study?
Does fetal heart rate variability analysis from phonocardiographic recordings using a neural network accurately classify fetal health status?
Does fetal heart rate variability analysis from phonocardiographic recordings using a neural network accurately classify fetal health status?
An algorithm using fetal heart rate variability analysis from phonocardiographic recordings and a neural network shows promise for non-invasive prediction of fetal wellbeing.
May support non-invasive fetal monitoring; leaves open prospective validation before clinical adoption.
This paper presents an algorithm for classification of fetal health status using fetal heart rate variability (fHRV) analysis through phonocardiography. First, the fetal heart sound signals are acquired from the maternal abdominal surface using a specially developed Bluetooth-based wireless data recording system. Then, fetal heart rate (FHR) traces are derived from these signals. Ten numbers of linear and nonlinear features are extracted from each FHR trace. Finally, the multilayer perceptron (MLP) neural network is used to classify the health status of the fetus. Results show very promising performance toward the prediction of fetal wellbeing on the set of collected fetal heart sound signals. Finally, this work is likely to lead to an automatic screening device with additional potential of predicting fetal wellbeing.
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Chourasia et al. (2011) studied Fetal health status. Fetal heart rate variability (fHRV) analysis through phonocardiography was evaluated on Classification of fetal health status. An algorithm using fetal heart rate variability analysis from phonocardiographic recordings and a multilayer perceptron neural network showed promising performance for predicting fetal wellbeing.
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