Influence of averaged fetal heart rate in heart rate variability analysis

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. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.

show abstract

“… Durosier et al (2014) found that the root mean square of the successive differences (RMSSD) of FHR estimated from

had worse discriminability than when estimated from

. Similarly, it has been suggested that HF

features are less discriminative than from

( De Jonckheere et al, 2019 ). The decreased discriminability of fHRV features, along with the undisclosed differences in commercial

estimation algorithms, will likely affect the performance of ML classifiers.…”

Section: Introductionmentioning

confidence: 99%