Chronic bradycardia, or slowing of heart rate, is common in preterm infants, and may often lead to neuropsychiatric disorders, developmental problems, and impaired cognitive functions in the long term. Therefore, early detection and treatment of bradycardia is important. To this end, we present a system identification-based approach to prediction of bradycardia in preterm infants. This algorithm is based on the notion that the cardiovascular system can be treated as a dynamic system, and that under bradycardia, this system reacts abnormally due to temporal and spatial destabilization. This paper presents a proof-of-concept of the proposed methodology by testing its performance using ECG data collected from ten preterm infants. We show that the proposed algorithm is correctly able to predict bradycardia occurrences (mean area under the ROC curve = 0.782 and variance = 0.0039) while minimizing the training or burn-in period. The physical interpretation of the results using the system dynamics approach is discussed. The developed algorithm performs well on not only classifying normal to abnormal conditions, but also showing a trend of transition between the two conditions. Future work is also discussed to further improve the algorithm and implement the algorithm in the neonatal intensive care unit. Our proposed method is able to predict bradycardia using only ECG data with minimal training period, and can be integrated into an automated system for bradycardia detection and treatment, and therefore, reduce the risks related to bradycardia in preterm infants.