Assessing the Effectiveness of Heart Rate Variability as A Diagnostic Tool for Brain Injuries in Infants

Hypoxic-Ischemic Encephalopathy (HIE), marked by cerebral oxygen deprivation, prompts exploration beyond the electroencephalogram (EEG) modality. This study investigates heart rate variability (HRV) to assess its potential for seizure detection and HIE grading for neonates. This study utilizes two annotated datasets from real-world clinical settings. Heart Rate (HR) is calculated from the Electrocardiogram (ECG) signal, which are then denoised and segmented. Sixteen time and frequency domain features are extracted from each HR segment. Employing Random Forest (RF), Support Vector Machine (SVM), and Isolation Forest (IF) classifiers, the investigation addresses the detection of seizure and nonseizure segments in ECG, alongside categorizing HIE severity into two mild and normal or moderate and severe grades. While the patient-independent evaluation of the seizure detection system reveals promising outcomes for specific cases, there is a requirement for further refinement in this aspect and exploration into the correlation between HR and EEG, considering the modest AUC of 68.54 percent gained across the entire dataset. In contrast, the HIE grading results present a more promising scenario, attaining an AUC of 77.13 percent. This emphasizes the efficacy of the HIE grading system as a significant diagnostic tool, suggesting its potential for broader clinical applications.