We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small amount of individual data, which is a binary one-against-the-rest classifier, providing discrimination between normal and abnormal patterns from that individual. Such an individual-specific classifier can be a two-class classifier or a one-class classifier, depending on the availability of abnormal patterns in the individual training dataset. The classifications from the two classifiers are fused to obtain a final decision. The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods. The proposed method can also be useful in anomaly detection of other biomedical signals.