Deep Learning in Healthcare
An explainable deep learning framework for trustworthy arrhythmia detection from ECG signals
This paper presents an explainable deep learning framework for accurate and reliable arrhythmia detection from ECG signals. The innovative approach integrates advanced DL architectures (CNN and DNN) within a multi-stage pipeline, encompassing meticulous data preparation, state-of-the-art signal preprocessing, and robust multi-strategy data balancing techniques (ADASYN, SMOTE, SMOTETomek, ROS). Crucially, it incorporates Explainable Artificial Intelligence (XAI) methodologies (SHAP, LIME, FIA) to provide transparent insights into the model's decision-making process. Rigorous evaluation on benchmark ECG datasets (MITDB, PTBDB, NSTDB) demonstrates superior classification accuracy, with the ROS+CNN model achieving 99.74%, 99.43%, and 99.98% respectively. The embedded XAI components offer actionable interpretability, fostering clinical trust and paving the way for more reliable and impactful AI-driven cardiovascular diagnostics.
Impact Metrics & Business Value
Our explainable deep learning framework for arrhythmia detection offers profound business value by improving diagnostic accuracy, reducing computational demands, and increasing clinical trust. The superior accuracy (up to 99.98%) directly translates to better patient outcomes and reduced healthcare costs associated with misdiagnosis. By leveraging efficient CNN and DNN architectures and robust data balancing, the framework ensures high performance without excessive computational demands, making it suitable for real-time applications. The integration of XAI provides clinicians with transparent, actionable insights, accelerating adoption and ensuring responsible AI deployment in critical medical diagnostics.
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Deep Analysis & Enterprise Applications
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Optimal Model Performance
Our CNN model, specifically when combined with Random Over-Sampling (ROS), consistently outperformed other architectures and balancing techniques across all benchmark datasets. This highlights the CNN's superior ability to capture complex spatial and temporal features within ECG signals.
99.98%
Peak Accuracy (NSTDB)
Enterprise Process Flow
Data Acquisition (WFDB Format)
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Data Preprocessing (Filtering, Normalization)
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Data Balancing (ROS, SMOTE, ADASYN)
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Deep Learning Model Training
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XAI Interpretation & Validation
XAI Integration for Clinical Trust
A key differentiator is the explicit integration of XAI methods (SHAP, LIME, FIA), providing transparent insights into the model's decision-making. This fosters clinical trust and enables validation of algorithmic predictions, bridging the gap between high-performing black-box algorithms and real-world clinical trustworthiness.
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Case Study: MITDB Dataset Performance
Our framework achieved superior performance on the MITDB dataset, a benchmark for real-world heartbeat morphologies. The ROS+CNN model achieved an outstanding Accuracy of 99.74%, Precision of 99.75%, TPR of 99.73%, and an F1-Score of 99.74%. It demonstrated exceptional discriminatory power with an AUC of 99.98% and minimal prediction errors (MAE 0.31, MSE 0.23, RMSE 4.78).
- Superior Accuracy: The ROS+CNN model achieved 99.74% accuracy, outperforming other methods.
- High Reliability: With an AUC of 99.98% and strong MCC/Cohen's Kappa (99.48%), the model demonstrates robust classification reliability.
- Minimal Errors: Exceptionally low MAE (0.31), MSE (0.23), and RMSE (4.78) indicate precise predictions.
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