ECG Heartbeat Classification: A Deep Transferable Representation (1805.00794v2)

Abstract: Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.

• The paper presents a deep CNN with residual connections for arrhythmia classification, achieving 93.4% accuracy on unseen samples.
• It employs transfer learning to repurpose learned features for myocardial infarction prediction, reaching 95.9% accuracy on single-lead ECG.
• The methodology enhances diagnostic scalability and resource efficiency, offering a robust framework for telemedicine applications.

ECG Heartbeat Classification: A Deep Transferable Representation

This paper presents a sophisticated approach to ECG heartbeat classification through the development of a convolutional neural network (CNN)-based methodology. The research aims to accurately classify cardiac arrhythmias and facilitate knowledge transfer to myocardial infarction (MI) prediction. Employing the PhysioNet MIT-BIH Arrhythmia and PTB Diagnostic ECG Datasets, the paper demonstrates a robust framework for both tasks, adhering to the AAMI EC57 standard for classification.

The motivation for this research arises from the limitations of manual ECG signal analysis, which is often error-prone and time-consuming due to the complexity of the time-series data involved. To overcome these challenges, the authors propose a deep learning framework with a focus on developing transferable representations. The central innovation lies in using deep CNNs with residual connections to categorize arrhythmic heartbeats and extend this trained model to MI classification tasks, addressing the need for a scalable, automated, and accurate solution.

Methodology and Experiments

The methodology consists of preprocessing ECG signals, training a CNN on arrhythmia classification, and leveraging the learned representations for MI prediction. The authors introduce a preprocessing step to extract beats from ECG signals efficiently, emphasizing its generality across different morphological data without prior assumptions. The proposed neural architecture is a deep residual CNN with 32 kernels per convolutional layer, which achieved a notable 93.4% average accuracy on arrhythmia classification.

On the arrhythmia classification task, the model was tested on 4,079 previously unseen samples, revealing competitive performance metrics within the state-of-the-art range, as demonstrated through confusion matrices and accuracy comparisons. The paper further illustrates the adaptability of learned representations through transfer learning for MI classification, obtaining a 95.9% accuracy on the PTB data using only single-lead input, underscoring its practical implications.

Numerical Results and Implications

The paper reports strong numerical results, with the CNN's ability to predict heartbeat categories and MI conditions achieving accuracy levels on par or better than referenced works using traditional or deep network approaches. In arrhythmia classification, the paper achieves an average accuracy comparable with prior studies that utilize different methodologies including support vector machines and decision trees.

Transference of knowledge to MI classification showcases the versatility and potency of the model, reaching performance levels close to those achieved using 12-lead ECG inputs while only using a single-lead ECG. This demonstrates a significant improvement in resource efficiency without compromising accuracy, highlighting an essential application for telemedicine and less hardware-intensive healthcare solutions.

Conclusion and Future Directions

The research exemplifies the applicability of deep learning and transfer learning within health informatics, providing an effective framework for complex diagnostic tasks in cardiac care. By instituting a transferable representation, this work broadens the scope for how ECG data can be leveraged across different cardiac conditions. The results suggest promising avenues for future explorations, including extending the model's learnability for other diseases or applying similar techniques for real-time, patient-specific diagnostic tools in clinical settings.

Furthermore, the paper sets a precedent for integrating deep learning frameworks into medical diagnostics, inviting further exploration into the fusion of AI methodologies and healthcare applications. Future work might investigate the extension of these principles to other medical time-series data types or the incorporation of multimodal datasets to enhance diagnostic accuracy and efficiency even further.