Abstract
Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.
Highlights
Algorithms for peak detection in photoplethysmogram (PPG) signals have been in use for a long time
Depending on the type of arrhythmia detected, the PPG peak detection may be comprised of the following combinations: only waveform envelope peak detection (WEPD) is included in SWEPD when PPG data are detected as atrial fibrillation (AF) after general noise detection; or WEPD and differentiator-adaptive threshold peak detection (DATPD) are both included in SWEPD when PPG data are detected as non-AF after general noise detection
The implementation of the seven comparison methods is available on https://biosignal.uconn.edu/resources/( access on 31 June 2021). We show both root mean squared error (RMSE) values of heart rates and IBI for all arrhythmias considered for our three methods and the seven other compared methods
Summary
Algorithms for peak detection in photoplethysmogram (PPG) signals have been in use for a long time. What we have developed in our current work, which no other authors have reported, is the ability to capture accurate PPG peak detection of fast heart rates (that is, to accurately estimate heart rates) due to either rapid ventricular responses, or premature atrial or ventricular contractions, using smartwatches. Existing PPG-based rhythm analysis algorithms may be adequate for AF detection but may not provide accurate heart rate estimation for patients in AF with fast heart rates due to rapid ventricular responses, or patients with premature atrial/ventricular contractions. This is an important limitation since resting heart rate control is a key treatment target for AF patients
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