Clustering ECG heartbeat using improved semi‐supervised affinity propagation

The electrocardiogram (ECG) has become an important tool for the diagnosis of cardiovascular diseases. As long‐term ECG recordings become more common, driven partly by the development of intelligent hardware, the requirement for automatic ECG analysis continues to grow. Research has attempted to use the expert knowledge to optimise ECG‐related algorithms, however, visual analysis of long‐term ECG is tedious and operator dependent. In previous studies, an ECG beat clustering approach based on self‐organising maps has been applied to reduce the amount of time the operator must to spend. This unsupervised approach partitions the ECG beats into 25 groups, however, the cluster number (25) does not accurately reflect the actual number of categories. In this study, an integrated method is presented for the clustering of ECG beats based on an improved semi‐supervised affinity propagation algorithm with independent component analysis. Using the MIT‐BIH arrhythmia database, the authors find that the resulting clusters to exhibit a high degree of precision. The integrated method outperforms other conventional methods in the MIT‐BIH database, and has great theoretical and practical significance in the field of cardiac disease.