Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification

Abstract

Automatic heartbeat classification technology based on the ECG plays an important role in assisting doctors with arrhythmia diagnosis. While many heartbeat classification studies can achieve good performance under the intra-patient paradigm, they still cannot offer acceptable classification results under the inter-patient paradigm. Additionally, the available ECG datasets are highly class imbalanced since normal heartbeats appear much more frequently than abnormal heartbeats, resulting in most methods having low sensitives and positive predictive values on minority class ectopic heartbeats. To solve the above problems, this study proposes an automatic ECG heartbeat classification method based on ensemble learning and multi-kernel learning. First, we use a linear combination of the radial basis function kernel and the polynomial kernel to produce a mixed-kernel-based extreme learning machine (MKELM). Then, a MKELM-based random forest binary classifier (MKELM-RF) is constructed. Finally, an ensemble multiclass classifier MKELM-RF-OVO is proposed based on one-vs.-one (OVO) reduction and MKELM-RF. We evaluated the proposed method on the public MIT-BIH-AR benchmarks database, under the inter-patient paradigm, classifying four types of heartbeats, namely, normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V) and the fusion of ventricular and normal (F). The obtained overall accuracy and the average positive predictive value are 98.1% and 93.9%, respectively, which are higher than the current studies by approximately 4% and 6%, respectively. The sensitivities for classes S and V are 1 and 94.4%, respectively, which outperforms most methods. The evaluation results show that our proposed method achieves a superior classification performance compared to the state-of-the-art methods.