Most, if not all, binary patterns variants consider signals observations separately; hence, binary patterns variants ignore any relationship among observations. In this paper we proposed an algorithm that enhances binary patterns extraction to accommodate for temporal progression changes. The enhanced binary patterns feature extraction extracts a single feature vector that captures changes occurred to observations over time. This enhancement is crucial in cases where the examined signal is repetitive in nature, such as ECG signal. Enhanced binary patterns were examined for ECG biometric application on ECG database with 1,012 subjects. The enhanced binary patterns achieved an EER of 7.89% in comparison to 12.4% and 12.3% for non-enhanced binary patterns and a state of the art work. We also showed that enhanced binary patterns features are capable to extract discriminative ECG features that reduced EER by 15% when compared to time-domain raw samples.