Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate

Abstract

Objective: Episodes of bradycardia are common and recur sporadically in preterm infants, posing a threat to the developing brain and other vital organs. We hypothesize that bradycardias are a result of transient temporal destabilization of the cardiac autonomic control system and that fluctuations in the heart rate signal might contain information that precedes bradycardia. We investigate infant heart rate fluctuations with a novel application of point process theory.

Methods: In ten preterm infants, we estimate instantaneous linear measures of the heart rate signal, use these measures to extract statistical features of bradycardia, and propose a simplistic framework for prediction of bradycardia.

Results: We present the performance of a prediction algorithm using instantaneous linear measures (mean area under the curve = 0.79 ± 0.018) for over 440 bradycardia events. The algorithm achieves an average forecast time of 116 s prior to bradycardia onset (FPR = 0.15). Our analysis reveals that increased variance in the heart rate signal is a precursor of severe bradycardia. This increase in variance is associated with an increase in power from low content dynamics in the LF band (0.04-0.2 Hz) and lower multiscale entropy values prior to bradycardia.

Conclusion: Point process analysis of the heartbeat time series reveals instantaneous measures that can be used to predict infant bradycardia prior to onset.

Significance: Our findings are relevant to risk stratification, predictive monitoring, and implementation of preventative strategies for reducing morbidity and mortality associated with bradycardia in neonatal intensive care units.

Fig. 1

(a) An example of severe bradycardia for infant 7. The gray region represents a 3-minute control window, and the red region represents a 3-minute pre-bradycardia window. Statistical fluctuations of the point process estimation of R-R intervals from these two regions are used to evaluate the likelihood of an impending bradycardia. (b) Normalized mean and normalized variance of the point process indices from 7 events of infant 7. The pre-bradycardia window indices cluster distinctly from the control window. (c) The resulting average cumulative density of the indices from the cluster map.

Fig. 2

(a) The green areas denote regions that encompass a bradycardia. Notice that secondary bradycardias (8–9 min) are classified as one event. The other regions (gray) represent regions absent bradycardia. (b) Detailed diagram of one particular bradycardia segment. The parameters used for the algorithm are outlined in the Table 2. A prediction is triggered with a time stamp of the leading edge of the evaluation window (green arrow).

Fig. 3

Schematic of the prediction algorithm. For each subject, a portion of the ECG signal (beige) is used to create the classification models, while the remaining signal (green) is used for prediction.

Fig. 4

(a) Prediction outcome for Infant 7 (ε = 3 min, δ = 5 s, τ_r = δ). The instantaneous M(t) and V(t) indices from the evaluation window (EW, blue region) are calculated and used to predict bradycardia. The red and green blocks (bottom) denote false and positive predictions, respectively, at a FPR of 0.15. (b) The heart rate and inter-beat interval data are depicted as black, and the point process estimation is depicted as green. (c) A cumulative density curve for the evaluation window (blue curve) is compared to the training models. In this instance, the evaluation curve satisfies a threshold to trigger a prediction.

Fig. 5

ROC curve for infant 7. The dashed line represents an algorithm preforming by chance (AUC of 0.5). We observe a mean AUC of 0.79±0.018 for 444 bradycardia events. The severity performance is also given. Note “—“ denotes no events. *Infant 8 exhibited frequent single skipped-beat episodes that led to instantaneous bradycardia.

Fig. 6

Quantile representation of the earliest prediction time in a 3-minute window preceding bradycardias. We observe a mean forecast time of 116 seconds across 10 infants.

Fig. 7

Mean AUC of prediction algorithm with a varying refractory period. As the refractory period increases, the AUC decreases. There is a trade-off between the performance of the algorithm and the waiting time after predictions.

Fig. 8

We observe an increase in the average instantaneous variance measure just prior to bradycardia onset. The table details the average eigenvalues (λ) from PCA analysis of the (M(t), V(t)) clusters prior to bradycardias. We observe statistical significance between variance of normal and severe segments.

Fig. 9

(a) Temporal evolution of frequency content of R-R time series prior to bradycardia, with a Morlet wavelet transform. (b) Severe bradycardias exhibit decreased power in the LF content compared to normal heart rate segments. (c) We observe decreased sample entropy across all time scales.