Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.

Abstract

Background: Deep neural networks (DNNs) have recently been significantly applied to automatic arrhythmia classification. However, their classification accuracy still has room for improvement.

Objectives: The aim of this study is to address the existing limitations in current models by developing a more effective approach for automatic arrhythmia classification. The specific objectives include enhancing the receptive field sizes to capture more detailed information across various temporal scales, and incorporating inter-channel correlations to improve the feature extraction process.

Methods: This study proposes a pyramidal dense connectivity layer and bidirectional long short-term memory network (PDC-BiLSTM) to effectively extract waveform features across various temporal scales, which can capture the intricate details and the broader global information in the signals through a wide range of sensory fields. The efficient channel attention (ECA) is additionally introduced to dynamically allocate weights to each feature channel, assisting the model inefficiently prioritizing essential characteristics during the training process.

Results: The experimental results on the MIT-BIH arrhythmia database showed that the overall classification accuracy of the proposed method under the intra-patient paradigm reached 99.82%, and the positive predictive value, sensitivity and F1 Score were 99.64%, 97.61% and 98.60% respectively; under the inter-patient paradigm, the overall accuracy was 96.30%.

Conclusion: Compared with the latest research results in this field, the proposed model is also better than the existing models in terms of accuracy, which has the potential value of being applied to devices that assist in diagnosing cardiovascular diseases.