Amplitude and Frequency Modulation of EEG Predicts Intraventricular Haemorrhage in Preterm Infants

Abstract

Background: Intraventricular hemorrhage (IVH) is a common and significant complication in premature infants. While cranial ultrasound is the golden standard for IVH detection, it may not identify lesions until hours or days after occurring, which limits early intervention. Predicting IVH in premature infants would be highly advantageous. Recent studies have shown that EEG data's amplitude and frequency modulation features could offer predictive insights for neurological diseases in adults. Methods: To investigate the association between IVH and EEG monitoring, a retrospective case-control study was conducted in preterm infants. All infants underwent amplitude integrated EEG monitoring for at least 3 days after birth. The study included 20 cases who had an IVH diagnosed on cranial ultrasound and had a negative ultrasound 24h earlier, and 20 matched controls without IVH. Amplitude and frequency modulation features were extracted from single-channel EEG data, and various machine learning algorithms were evaluated to create a predictive model. Results: Cases had an average gestational age and birth weight of 26.4 weeks and 965 grams, respectively. The best-performing algorithm was adaptive boosting. EEG data from 24 hours before IVH detection proved predictive with an area under the receiver operating characteristic curve of 93%, an accuracy of 91%, and a Kappa value of 0.85. The most informative features were the slow varying instantaneous frequency and amplitude in the Delta frequency band.