New perspectives in electronic fetal surveillance

Abstract

Despite its recognized limitations, fetal heart rate monitoring is a mainstay of intrapartum care. Although the basic technology in standard electronic fetal monitors has changed little in recent decades, clinical behavior in response to heart rate monitoring has changed considerably. In addition to clearly defi ned nomenclature and clinical guidelines, there is an increased awareness that environmental and human factors can impair clinical judgment, resulting in delayed intervention and, consequently, birth-related injury. This review examines three essential steps that affect clinical outcome: (1) signal acquisition, (2) associations with physiological outcome, and (3) clinical intervention. Only the third step is directly responsible for changing clinical outcome. However, timely initiation of interventions is dependent upon the second step, which is dependent upon the fi rst step. Thus, defi ciencies at each step tend to accumulate and contribute to the worsening of overall clinical outcome. This review article summarizes advances occurring at each step. The synergy and convergence of innovations in engineering, mathematics, and behavioral science shows considerable promise in intrapartum fetal surveillance.

The limits of electronic fetal heart rate monitoring in the prevention of neonatal metabolic acidemia

“…More discriminating signal processing algorithms such as heart rate variability at various frequencies, subtle heart rate responses relative to contractions, or fractal analysis of variability are also under investigation. [17][18][19]…”

Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

“…Besides poor standard of interpretation and high intra/inter observer variability [2], CTG delivers only the momentary heart rate with limited accuracy [7]. Therefore, CTG derived fetal heart rate time-series is not suitable for beat-to-beat analysis, which is clinically more significant than long-term measure [8,9]. Fetal magnetocardiogram (fMCG) overcomes these limitations of CTG and provides a non-invasive approach for monitoring fetal cardiac activity with sufficient precision [10,11].…”

Machine Learning in Fetal Cardiology: What to Expect

“…The best results were obtained by Iraji et al [51] using the Portuguese database showing an accuracy of 99.5%. There have also been some attempts to translate this into clinical practice via the development of software such as Infant, PeriCALM [52,53], and Foetos [54] or the development of mobile/website applications [55,56] to provide additional support in the interpretation of CTG signals and therefore to improve the assessment of fetal status. However, there is no evidence on whether these systems really improve the prediction of fetal distress or acidemia compared to visual CTG interpretation alone, and reports about their clinical performance were not found.…”