Multilayer extreme learning machine-based unsupervised deep feature representation for heartbeat classification

Heartbeat classification plays an important role in identifying cardiac arrhythmias. Although automated heartbeat classification approaches have been broadly reported, they still suffer from some disadvantages, such as the inescapable domain knowledge and human ingenuity for hand-crafted heartbeat feature engineering, and the slow learning speed for iterative deep learning heartbeat classification approach. To address the above issues, for the first time, this paper proposes a novel multilayer extreme learning machine (ML-ELM) heartbeat classification approach for feature representation and classification of the heartbeat signals. Different from the iterative optimization scheme in traditional neural networks, ELM autoencoder (ELM-AE) is employed in ML-ELM for random mapping of the input weights and non-iterative learning is implemented for output weights. To realize unsupervised self-encoding heartbeat feature mapping, the output of ELM-AE is consistent with the incoming heartbeat signal, and the optimization objective is to minimize the reconstruction error. Heartbeat feature extraction is efficiently performed by layer-by-layer ELM-AE stacking. In the stage of heartbeat classification, to solve the unstable performance caused by random parameter initialization in ELM hidden nodes, bagging-based ensemble learning is employed to combine several ELM classifiers and thus develops a well-performed heartbeat classification approach. The proposed approach is applied to two-lead ECG signals, which is obtained from the MIT-BIH arrhythmia public dataset. The experimental results show that the ELM-AE based feature extraction can effectively characterize the characteristics of the heartbeat signal with high efficiency compared with other state-of-the-art approaches. Applying the ensemble decision fusion to two leads, the final classification accuracy reaches 99.41%.