A Q-transform-based deep learning model for the classification of atrial fibrillation types.

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Imaging

Background:

• Atrial Fibrillation (AF) is a growing global health concern, necessitating improved diagnostic tools for its subtypes.
• Accurate classification of AF subtypes is crucial for effective clinical management and personalized treatment strategies.
• Current diagnostic methods struggle with precise differentiation between AF subtypes.

Purpose of the Study:

• To develop and evaluate a custom 2D Convolutional Neural Network (CNN) model for automatic classification of Non-Atrial Fibrillation (Non-AF), Paroxysmal Atrial Fibrillation (PAF), and Persistent Atrial Fibrillation (PsAF) from ECG images.
• To assess the model's performance in differentiating these AF subtypes using key accuracy metrics.
• To explore the potential of AI in enhancing the diagnosis and management of AF.

Main Methods:

• ECG signals were transformed into time-frequency representations using Constant Q-Transform (CQT), generating a large dataset of images.
• A custom six-layer 2D CNN model was developed for image classification.
• Data augmentation techniques were employed to address class imbalance, followed by training, validation, and testing with a 0.7:0.15:0.15 ratio.

Main Results:

• The proposed 2D CNN model achieved high performance across all metrics: 0.98 accuracy, 0.98 precision, 0.98 sensitivity, 0.97 specificity, and 0.98 F1-score.
• The model demonstrated a strong ability to differentiate between Non-AF, PAF, and PsAF rhythms from ECG-derived images.
• The dataset comprised over 150,000 images, with training, validation, and testing sets carefully partitioned.

Conclusions:

• The developed AI model shows significant potential for accurately classifying AF subtypes from ECG images.
• This technology can assist physicians in reducing misdiagnosis and tailoring personalized treatment plans for AF patients.
• The findings highlight the efficacy of deep learning in advancing cardiac arrhythmia diagnostics.