An automatic Premature Ventricular Contraction detection method using photoplethysmographic signals achieved sensitivities of 96.05% and 95.37% and specificities of 99.85% and 99.80% for two PVC types.
Does an automatic detection method using PPG signals accurately detect Premature Ventricular Contractions compared to ECG?
An Artificial Neural Network-based algorithm using PPG signals can reliably detect premature ventricular contractions with high sensitivity and specificity.
The purpose of this study was the development and investigation of the automatic Premature Ventricular Contraction (PVC) detection and classification method using Photoplethysmographic (PPG) signals. The main issue of using PPG for arrhythmia detection are the artefacts which may be falsely detected as an arrhythmic pulses. The method is based on 6 PPG features, obtained in 12 s analysis frame. The features are peak-to-peak intervals and PPG power derived features. The fundamental frequency of the PPG was used for feature extraction and normalization. The Artificial Neural Network with back-propagation was used for the PPG pulse classification. The PPG signals from Physionet MIMIC II and MIMIC databases were used for algorithm training and testing. PPG were annotated by referring to synchronously registered ECG signals. The method was evaluated by calculating sensitivity and specificity which for the two main PVC types are 96,05 / 95,37 % and 99,85 / 99,80 %, respectively. The study results suggest that PPG can be used for the reliable PVC detection.
Sološenko et al. (Wed,) conducted a other in Premature Ventricular Contraction. Automatic PVC detection and classification method using PPG signals vs. Synchronously registered ECG signals was evaluated on Sensitivity and specificity for PVC detection. An automatic Premature Ventricular Contraction detection method using photoplethysmographic signals achieved sensitivities of 96.05% and 95.37% and specificities of 99.85% and 99.80% for two PVC types.