This work investigates the efficacy of heart rate (HR) based measures for patient-independent, automatic detection of seizures in newborns. Sixty-two time-domain and frequency-domain features were extracted from the neonatal heart rate signal. These features were classified using a sophisticated support vector machine (SVM) scheme. The performance was evaluated on a large dataset of 208 h from 14 newborn infants. It was shown that the HR can be useful for the detection of neonatal seizures for certain patients yielding an area under the receiver operating characteristic (ROC) curve of up to 82%. On evaluating the system using multiple patients an average ROC area of 0.59 with sensitivity of 60% and specificity of 60%, were obtained. Feature selection was performed and in the majority of patients the performance was degraded. Further analysis of the feature weights found significant variability in feature ranking across all patients. Overall, the patient-independent system presented here was seen to perform well in some patients (2 out of 14) but performed poorly when tested on the entire group.