Why the study?
Burst AF does not show symptoms and is difficult to detect using ECG, making PPG an easy-to-record alternative for long-term monitoring.
Does a VMD-based classification framework using PPG sensor data improve the accuracy of automated atrial fibrillation detection compared to other state-of-the-art methods?
Does a VMD-based classification framework using PPG sensor data improve the accuracy of automated atrial fibrillation detection compared to other state-of-the-art methods?
A novel VMD-based classification framework using PPG sensor data demonstrates high accuracy (99.08%) for automated detection of atrial fibrillation, suggesting potential for real-time clinical and long-term monitoring applications.
Atrial fibrillation (AF) is one of the most common arrhythmia. AF can be a reason for strokes and damage to heart activities. The electrocardiogram (ECG) is commonly used for AF detection. It should be noted that the burst AF does not show any symptoms and is difficult to detect using ECG. As an alternative to ECG, photoplethysmography (PPG) is used for AF diagnosis, which is easy to record and suitable for long-term monitoring. This letter proposes a new classification framework for automated AF detection based on variational mode decomposition (VMD). The proposed framework has been studied on a publicly available dataset. The proposed VMD-based classification framework outperformed other state-of-the-art methods used for AF detection and achieved the highest accuracy of 99.08% using ten-fold cross-validation. The results of the proposed methodology have shown significant improvement, and the developed sensor-based system has proved suitability to real-time clinical practices.
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Pachori et al. (2024) studied this question.
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