| Title |
Training convolutional neural networks on simulated photoplethysmography data: application to bradycardia and tachycardia detection / |
| Authors |
Sološenko, Andrius ; Paliakaitė, Birutė ; Marozas, Vaidotas ; Sörnmo, Leif |
| DOI |
10.3389/fphys.2022.928098 |
| Full Text |
|
| Is Part of |
Frontiers in physiology.. Lausanne : Frontiers media S.A.. 2022, vol. 13, art. no. 928098, p. 1662-1670.. ISSN 1664-042X |
| Keywords [eng] |
bradycardia ; convolutional neural networks ; detection ; photoplethysmogram ; simulated signals ; tachycardia |
| Abstract [eng] |
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. |
| Published |
Lausanne : Frontiers media S.A |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
