Counting fetal movements of small-for-gestational infants using a fetal movement acceleration measurement recorder

Maternal perception of decreased fetal movements is independent of infant size

OBJECTIVE

Fetal movements are often used as a surrogate for fetal wellbeing. Previous research suggests a link between maternal perception of decreased fetal movements (DFM) and small-for-gestational-age (SGA) infants. The aim of this study was to investigate the association between maternal presentation with DFM and birth-weight centile categories at a large Australian perinatal center.

METHODS

This was a retrospective study of non-anomalous singleton infants born at ≥ 28 + 0 weeks' gestation between January 2016 and October 2020 at the Mater Mothers' Hospital in Brisbane, Australia. The primary outcome was the rate of DFM according to birth-weight centile category. Maternal demographic characteristics included age, body mass index, ethnicity, parity, medical conditions and previous stillbirth. The association between DFM and birth-weight centile was evaluated using adjusted multinomial regression models. Robust standard errors were used to account for clustering at the patient level. Wald tests and Akaike's and Bayesian information criteria were used to evaluate models.

RESULTS

Over the 5-year study period, 45 042 women met the inclusion criteria. Of these, 6690 (14.9%) women presented with DFM. Of the DFM cohort, 80.9% (5411/6690) had only one presentation with DFM, and 19.1% (1279/6690) had two or more presentations. The overall stillbirth rate was similar in women with DFM (0.1% (8/6690)) and those without DFM (0.1% (50/38 352)). There was no association between DFM (either single or multiple) and infant birth-weight centile.

CONCLUSIONS

This study suggests that presentation with DFM is not associated with infant size. Clinicians should consider additional risk factors and the overall clinical context when deciding appropriate management. DFM is not necessarily an indication for an immediate or urgent ultrasound scan to assess fetal size. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

Fetal movement is an important clinical indicator to assess fetus growth and development status in the uterus. In recent years, a noninvasive intelligent sensing fetal movement detection system that can monitor high-risk pregnancies at home has received a lot of attention in the field of wearable health monitoring. However, recovering fetal movement signals from a continuous low-amplitude background that is heavily contaminated with noise and recognizing real fetal movements is a challenging task. In this paper, fetal movement can be efficiently recognized by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light Gradient Boosting Machine (LightGBM) model. Firstly, the Kalman filtering (KF) algorithm is used to recover the fetal movement signal in a continuous low-amplitude background contaminated by noise. Secondly, the time domain, frequency domain, and wavelet domain (TFWD) features of the preprocessed fetal movement signal are extracted. Finally, the Bayesian Optimization algorithm (BOA) is used to optimize the LightGBM model to obtain the optimal hyperparameters. Through this, the accurate prediction and recognition of fetal movement are successfully achieved. In the performance analysis of the Zenodo fetal movement dataset, the proposed KF + TFWD + BOA-LGBM approach's recognition accuracy and F1-Score reached 94.06% and 96.85%, respectively. Compared with 8 existing advanced methods for fetal movement signal recognition, the proposed method has better accuracy and robustness, indicating its potential medical application in wearable smart sensing systems for fetal prenatal health monitoring.