A novel patient-specific ECG classification algorithm based on recurrent neural networks and density-based clustering achieved state-of-the-art classification performance on the MIT-BIH Arrhythmia Database.
Does a patient-specific ECG classification algorithm based on RNN and clustering improve classification performance on the MIT-BIH Arrhythmia Database?
A novel patient-specific ECG classification algorithm using RNN and density-based clustering achieves state-of-the-art performance on the MIT-BIH Arrhythmia Database.
In this paper, we propose a novel patient-specific electrocardiogram (ECG) classification algorithm based on the recurrent neural networks (RNN) and density based clustering technique. We use RNN to learn time correlation among ECG signal points and to classify ECG beats with different heart rates. Morphology information including the present beat and the T wave of former beat is fed into RNN to learn underlying features automatically. Clustering method is employed to find representative beats as the training data. Evaluated on the MIT-BIH Arrhythmia Database, the experimental results show that proposed algorithm achieves the state-of-the-art classification performance.
Zhang et al. (Sun,) conducted a other in Arrhythmia. Patient-specific ECG classification algorithm based on RNN and density-based clustering was evaluated on Classification performance. A novel patient-specific ECG classification algorithm based on recurrent neural networks and density-based clustering achieved state-of-the-art classification performance on the MIT-BIH Arrhythmia Database.