AICRN: Attention-Integrated Convolutional Residual Network for Interpretable Electrocardiogram Analysis

TL;DR

This paper introduces AICRN, a novel deep learning architecture with attention mechanisms for interpretable and precise ECG parameter regression, improving cardiac analysis and reducing manual effort.

Contribution

The paper presents a new attention-integrated convolutional residual network architecture specifically designed for interpretable ECG analysis with superior regression accuracy.

Findings

AICRN outperforms existing models in ECG parameter regression.

The model enhances interpretability through spatial and channel attention mechanisms.

AICRN reduces manual effort in cardiac event detection.

Abstract

The paradigm of electrocardiogram (ECG) analysis has evolved into real-time digital analysis, facilitated by artificial intelligence (AI) and machine learning (ML), which has improved the diagnostic precision and predictive capacity of cardiac diseases. This work proposes a novel deep learning (DL) architecture called the attention-integrated convolutional residual network (AICRN) to regress key ECG parameters such as the PR interval, the QT interval, the QRS duration, the heart rate, the peak amplitude of the R wave, and the amplitude of the T wave for interpretable ECG analysis. Our architecture is specially designed with spatial and channel attention-related mechanisms to address the type and spatial location of the ECG features for regression. The models employ a convolutional residual network to address vanishing and exploding gradient problems. The designed system addresses traditional analysis challenges, such as loss of focus due to human errors, and facilitates the fast and easy detection of cardiac events, thereby reducing the manual efforts required to solve analysis tasks. AICRN models outperform existing models in parameter regression with higher precision. This work demonstrates that DL can play a crucial role in the interpretability and precision of ECG analysis, opening up new clinical applications for cardiac monitoring and management.