Online detection of fetal acidemia during labour by testing synchronization of EEG and heart rate: a prospective study in fetal sheep

Abstract

Severe fetal acidemia during labour can result in life-lasting neurological deficits, but the timely detection of this condition is often not possible. This is because the positive predictive value (PPV) of fetal heart rate (FHR) monitoring, the mainstay of fetal health surveillance during labour, to detect concerning fetal acidemia is around 50%. In fetal sheep model of human labour, we reported that severe fetal acidemia (pH<7.00) during repetitive umbilical cord occlusions (UCOs) is preceded ∼60 minutes by the synchronization of electroencephalogram (EEG) and FHR. However, EEG and FHR are cyclic and noisy, and although the synchronization might be visually evident, it is challenging to detect automatically, a necessary condition for bedside utility. Here we present and validate a novel non-parametric statistical method to detect fetal acidemia during labour by using EEG and FHR. The underlying algorithm handles non-stationary and noisy data by recording number of abnormal episodes in both EEG and FHR. A logistic regression is then deployed to test whether these episodes are significantly related to each other. We then apply the method in a prospective study of human labour using fetal sheep model (n = 20). Our results render a PPV of 68% for detecting impending severe fetal acidemia ∼60 min prior to pH drop to less than 7.00 with 100% negative predictive value. We conclude that this method has a great potential to improve PPV for detection of fetal acidemia when it is implemented at the bedside. We outline directions for further refinement of the algorithm that will be achieved by analyzing larger data sets acquired in prospective human pilot studies.

Conflict of interest statement

Competing Interests: BSR and MGF are inventors of related patent applications entitled “EEG Monitor of Fetal Health” including U.S. Patent Application Serial No. 12/532,874 and CA 2681926 National Stage Entries of PCT/CA08/00580 filed March 28, 2008, with priority to US provisional patent application 60/908,587, filed March 28, 2007. This does not alter the authors' adherence to all PLOS ONE policies on sharing data and materials. MGF is a current Academic Editor of PLOS ONE. This does not alter the authors' adherence to PLOS ONE editorial policies and criteria.

Figures

Figure 1. The algorithm to detect EEG-FHR synchronisation.

Figure 2. Arterial blood gas values.

Mean±SD. UCO, umbilical cord occlusions; values are shown for each 20 min of UCO. * p

Figure 3. Representative behaviour of the electroencephalogram (EEG) and fetal heart rate (FHR) at baseline and during repetitive umbilical cord occlusions (UCO).

10 minutes of baseline (A), mild (B), moderate (C) and severe (D) UCO series are shown. X axis shows time of the day. The segment during the severe UCO series represents the stage when the adaptive brain shut-down pattern is visible in EEG in phase with FHR decelerations triggered by changes in the umbilical cord occluder pressure (UCP). Note the brief, ∼60 seconds lasting, episodes of EEG suppression during each UCO-induced FHR decelerations and EEG amplitude recovery between the UCOs.

Figure 4. A representative example of crossing point detection.

TOP: Single change point and crossing point detection. BOTTOM: Complete experimental recording demonstrating detection of EEG-FHR synchronisation based on the crossing point detection and subsequent validation using logistic regression analysis. Vertical black line denotes onset of EEG-FHR synchronization as per visual expert analysis. Vertical orange bar denotes the drop of pH to less than 7.00. The p-values over time are rendered by red lines where the null hypothesis (no EEG-FHR synchronization) was rejected, i.e., p less than 5% and yellow lines where p value was between 5% and 10%. Note, that three subsequent crossing point detections are required to consider identifying EEG-FHR synchronization. This corresponds to a window length of 10 min (cf. Fig. 1).