Classification of AAMI heartbeat classes with an interactive ELM ensemble learning approach

In recent years, the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation are closely followed as a possible solution for standardization. Regardless of the class normalization, this standard basically recommends for performance evaluation to adopt inter-patient scenarios, which renders the classification task very challenging due to the strong variability of ECG signals. To deal with this issue, we propose in this paper a novel interactive ensemble learning approach based on the extreme learning machine (ELM) classifier and the induced ordered weighted averaging (IOWA) operators. While ELM is adopted for ensemble generation the IOWA operators are used for aggregating the obtained predictions in a nonlinear way. During the iterative learning process, the approach allows the expert to label the most relevant and uncertain ECG heart beats in the data under analysis and then adds them to the original training set for retraining. The experimental results obtained on the widely used MIT-BIH arrhythmia database show that the proposed approach significantly outperforms state-of-the-art methods after labeling on average 100 ECG beats per record. In addition, the results obtained on four other ECG databases starting with the same initial training set from MIT-BIH confirm its promising generalization capability.