Key result
A new classification method using phase locking value correlation matrices from 12-lead ECG signals and an Rstnet18 convolutional neural network achieved promising performance for paroxysmal atrial fibrillation detection.
Why the study?
Paroxysmal atrial fibrillation is a common cardiac arrhythmia characterized by irregular atrial contractions, motivating new methods for its detection using surface 12-lead ECG signals.
Can a deep learning model using PLV correlation matrices from 12-lead ECG signals accurately detect paroxysmal atrial fibrillation?
Can a deep learning model using PLV correlation matrices from 12-lead ECG signals accurately detect paroxysmal atrial fibrillation?
A novel deep learning approach using phase locking value correlation matrices from 12-lead ECGs shows promise for automated detection of paroxysmal atrial fibrillation.
May aid automated paroxysmal AF detection on ECG; leaves open prospective validation before clinical use.
Paroxysmal atrial fibrillation (PAF) is a common cardiac arrhythmia characterized by irregular contractions of the atria. In this paper, we propose a new classification method based on surface 12-lead electrocardiography (ECG) signals by using statistical analysis and deep learning models for PAF detection. First, we extract signals with a frequency range of 1-40Hz from preprocessed ECG and divide them into three frequency bands. Secondly, we calculate the phase locking value (PLV) between the twelve channels of the ECG signals and generate a correlation matrix. After bilinear interpolation and binarization, the PLV correlation matrix is obtained as a feature and plotted as a feature map. Finally, we input feature maps of different frequency bands into the Rstnet18 convolutional neural network, and achieving a promising performance.
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Ma et al. (2023) studied Paroxysmal atrial fibrillation. Rstnet18 convolutional neural network based on PLV correlation matrix from 12-lead ECG was evaluated on PAF detection performance. A new classification method using phase locking value correlation matrices from 12-lead ECG signals and an Rstnet18 convolutional neural network achieved promising performance for paroxysmal atrial fibrillation detection.
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