Integrating Analytic and Appearance Attributes for Human Identification from ECG Signals

In this paper, we investigate identification of human subjects from electrocardiogram (ECG) signals. We segment the ECG records into individual heartbeat based on the localization of R wave peaks. Two types of features, namely analytic and appearance features, are extracted to represent the characteristics of heartbeat signal of different subjects. Feature selection is performed to find out significant attributes. We compared the performance of different classification algorithms. To better utilize the advantages of different types of features, we proposed two schemes for data fusion and classification. Our system achieves promising results with 100% correct human identification rate and 98.90% accuracy for heartbeat identification. The proposed framework reveals the potential of employing appearance based analysis in ECG signal, yet demonstrates the advantage of a hierarchical architecture in pattern recognition problems.