A Scalable Extreme Learning Machine (S-ELM) for Class-Incremental ECG-Based User Identification

User identification using electrocardiogram (ECG) is emerging due to the uniqueness and convenience of ECG signals. In addition, in real world applications, new subjects may enter the existed identification system and be authorized to access the private data. Therefore, we propose a scalable extreme learning machine (S-ELM) to meet the demand for class-incremental ECG-based user identification. We first prove that the output weight of the S-ELM learnt in a class-incremental manner is as same as that of a regular ELM which prior has the information of the number of total class. Therefore, in our experiment, S-ELM immunes from the catastrophic forgetting phenomenon, which is a common problem in class-incremental scenarios. Comparing to another class-incremental extreme learning machine such as progressive ELM, S-ELM outperforms progressive ELM by 7% accuracy in online dataset. Comparing to another commonly applied classifier, support vector machine (SVM) with linear and radial basis function (RBF) kernels, S-ELM shows its efficiency by 13.35% and 10.54% higher accuracy but only spends 5.09% and 3.48% of the inference time. Therefore, the proposed S-ELM is promising for the class-incremental ECG-based user identification.