Abstract
Fetal heart rate (FHR) data obtained from a non-stress test (NST) can be presented in a type of time series, which is accompanied by signal loss due to physical and biological causes. To recover or estimate FHR data, which is subjected to a high rate of signal loss, time series models [second-order autoregressive (AR(2)), first-order autoregressive conditional heteroscedasticity (ARCH(1)) and empirical mode decomposition and vector autoregressive (EMD-VAR)] and the residual bootstrap method were applied. The ARCH(1) model with the residual bootstrap technique was the most accurate [root mean square error (RMSE), 2.065] as it reflects the nonlinearity of the FHR data [mean absolute error (MAE) for approximate entropy (ApEn), 0.081]. As a result, the goal of predicting fetal health and identifying a high-risk pregnancy could be achieved. These trials may be effectively used to save the time and cost of repeating the NST when the fetal diagnosis is impossible owing to a large amount of signal loss.
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