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Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification
Insights
Deep learning models can now diagnose cardiac arrhythmia from ECGs. New methods, Grad-CAM and input deletion masks, offer interpretability, aligning AI predictions with medical knowledge for better clinical use.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Cardiac arrhythmia is a major cause of death, requiring early diagnosis.
- Traditional ECG analysis by cardiologists is limited by expert availability.
- Deep learning (DL) offers scalable arrhythmia diagnosis but lacks clinical interpretability.
Purpose of the Study:
- To develop interpretable DL models for cardiac arrhythmia classification.
- To correlate DL model outputs with specific ECG segments for clinical validation.
Main Methods:
- Applied Gradient-weighted Class Activation Mapping (Grad-CAM) for Convolutional Neural Network (CNN) interpretability.
- Utilized input deletion masks for Long Short-Term Memory (LSTM) model saliency analysis.
- Validated model competence against established baselines.
Main Results:
- Saliency visualizations provided insights into DL model predictions.
- Interpreted model outputs aligned with established medical literature on arrhythmia classification.
- Demonstrated the clinical relevance of DL interpretability in cardiology.
Conclusions:
- Developed and validated novel interpretability methods for DL-based arrhythmia detection.
- Enhanced clinical trust and utility of AI in diagnosing cardiac arrhythmias.
- Facilitated better correlation between AI findings and medical expertise for patient care.
Abstract:
Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis. However, these traditional methods require expert knowledge and are unable to model a wide range of arrhythmia. Recently, Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale. However, the black-box nature of these models prohibit clinical interpretation of cardiac arrhythmia. There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG. To this end, two methods are proposed to provide interpretability to the models. The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the second approach, saliency is derived by learning the input deletion mask for the LSTM model. The visualizations are provided on a model whose competence is established by comparisons against baselines. The results of model saliency not only provide insight into the prediction capability of the model but also aligns with the medical literature for the classification of cardiac arrhythmia.Clinical relevance- Adapts interpretability modules for deep learning networks in ECG arrhythmia classfication, allowing for better clinical interpretation.
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