A measure of channel importance is proposed for EEG-based detection of neonatal seizures. The channel weights are computed based on the integrated synchrony of classifier probabilistic outputs for the channels which share a common electrode. Those estimated weights are introduced within Bayesian probabilistic framework to provide a channel-specific and thus patient-adaptive seizure detection scheme. Results of validation on a clinical dataset of neonatal seizures confirm the utility of the proposed channel weighting for the SVM-based detection system recently developed in this research group. Exploiting the channel weighting, the ROC area can be drastically increased for the most difficult patients, with the average ROC area across 17 patients increased by 22% relative.