Abstract:
Enhanced real-time realizable AF algorithm for accurate detection of, and discrimination between, NSR, AF, PVC, and PAC. The method of these teachings includes an AF detection method having a modified Poincare approach in order to differentiate various patterns of PAC and PVC from NSR and AF. The method of these teachings can also apply to the Kullback-Leibler divergence or the Turning Point Ratio (TPR) to differentiate between various patterns of PAC and PVC from NSR and AF.
Abstract:
Methods and systems for automatic detection of Atrial Fibrillation (AF) are disclosed. The methods and systems use time-varying coherence functions (TVCF) to detect AF. The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs).
Abstract:
A real-time arrhythmia discrimination method is used in smartphones, which can discriminate between NSR, AF, PACs and PVCs using pulsatile time series collected from a smartphone's camera. To increase the sensitivity of AF detection and add the new capabilities of PVC and PAC identification, the arrhythmia discrimination method of these teachings combines Root Mean Square of Successive RR Differences (RMSSD), Shannon Entropy (ShE) and turning point ratio (TPR), with the Poincare plot, and utilizes the features of pulse rise/fall time and amplitude for arrhythmia discrimination.
Abstract:
A real-time arrhythmia discrimination method is used in smartphones, which can discriminate between NSR, AF, PACs and PVCs using pulsatile time series collected from a smartphone's camera. To increase the sensitivity of AF detection and add the new capabilities of PVC and PAC identification, the arrhythmia discrimination method of these teachings combines Root Mean Square of Successive RR Differences (RMSSD), Shannon Entropy (ShE) and turning point ratio (TPR), with the Poincare plot, and utilizes the features of pulse rise/fall time and amplitude for arrhythmia discrimination.
Abstract:
A pulse oximeter embedded with a motion and noise artifact (MNA) detection algorithm based on extraction of time-varying spectral features that are unique to the clean and corrupted components.
Abstract:
A real-time arrhythmia discrimination method is used in smartphones, which can discriminate between NSR, AF, PACs and PVCs using pulsatile time series collected from a smartphone's camera. To increase the sensitivity of AF detection and add the new capabilities of PVC and PAC identification, the arrhythmia discrimination method of these teachings combines Root Mean Square of Successive RR Differences (RMSSD), Shannon Entropy (ShE) and turning point ratio (TPR), with the Poincare plot, and utilizes the features of pulse rise/fall time and amplitude for arrhythmia discrimination.