Key result
A CNN-based detector trained on simulated PPG data achieved 98.1% sensitivity for bradycardia and 76.6% for tachycardia, outperforming a pulse-based reference detector.
Why the study?
The study was conducted to develop a method for detecting bradycardia and ventricular tachycardia using photoplethysmogram (PPG) signals.
Does a dual-branch convolutional neural network trained on simulated PPG data improve the detection of bradycardia and tachycardia compared to a pulse-based reference detector?
Does a dual-branch convolutional neural network trained on simulated PPG data improve the detection of bradycardia and tachycardia compared to a pulse-based reference detector?
A convolutional neural network trained on simulated PPG data can effectively detect bradycardia and ventricular tachycardia, offering higher sensitivity than a standard pulse-based detector.
Answers are grounded in this paper and cite their sources.
Should not yet change practice; leaves open generalization from simulated to real-world PPG data.
Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG). Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector. Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively. Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.
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Sološenko et al. (2022) studied Bradycardia and ventricular tachycardia (n=54). CNN-based detector vs. Pulse-based reference detector was evaluated on Sensitivity for bradycardia detection. A CNN-based detector trained on simulated PPG data achieved 98.1% sensitivity for bradycardia and 76.6% for tachycardia, outperforming a pulse-based reference detector.
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