xECGArch: A Multi-Scale Convolutional Neural Network CNN for Accurate and Interpretable Atrial Fibrillation Detection in ECG Analysis

xECGArch: A Multi-Scale Convolutional Neural Network CNN for Accurate and Interpretable Atrial Fibrillation Detection in ECG Analysis

Practical AI Solutions for Healthcare

Enhancing Cardiovascular Disease Detection with xECGArch

Deep learning methods, such as xECGArch, offer practical solutions for accurate and interpretable atrial fibrillation (AF) detection in ECG analysis. These methods excel in matching or surpassing the diagnostic performance of healthcare professionals, providing valuable insights for clinical integration.

Key Features of xECGArch

xECGArch uniquely separates short-term and long-term ECG features using two independent Convolutional Neural Networks (CNNs), optimizing for AF detection and achieving a 95.43% F1 score on unseen data. The architecture enhances interpretability and reliability, bridging the gap between clinical needs and automated analysis.

Dataset Utilization and Preprocessing

The study utilized extensive 12-lead ECG databases, ensuring applicability for portable devices and effectiveness in detecting AF. The datasets were balanced to address classifier bias and underwent preprocessing for noise reduction and scaling.

Interpretable ECG Analysis

The xECGArch integrates two independent 1D CNNs focusing on short-term and long-term ECG features, crucial for interpreting morphological and rhythmic patterns. Various xAI methods were employed and evaluated for interpretability, offering insights into the model’s decision-making by highlighting relevant features and contributions within the ECG data.

Multi-Scale Approach for AF Detection

The xECGArch’s combined short- and long-term CNNs enhance AF detection by leveraging distinct temporal features, achieving a high F1 score of 95.43%. Explanation methods like Deep Taylor Decomposition proved effective for interpreting model decisions, improving diagnostic accuracy and enhancing the interpretability of ECG analysis.

Evolution of AI in Healthcare

xECArch offers a practical AI solution for accurate and interpretable AF detection, providing valuable insights for healthcare professionals. Future applications include leveraging AI for other biosignals and improving big data cardiac screening through automated, trustworthy diagnostics.

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