Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures - Vargas-Calixto, Johann; Warrick, Philip; Kearney, Robert (Frontiers in Artificial Intelligence, 2021)
Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03 – 0.15 Hz), movement frequency power (MF: 0.15 – 0.5 Hz), high frequency power (HF: 0.5 – 1 Hz), the LF/(MF+HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF+HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. Furthermore, we found that the low frequency power, below 72 mHz, lost only 0.02% of its discriminability due to the AC method. Thus, information in frequencies below 72 mHz is robust to the AC method and additive noise.