A real-time ppg peak detection method for accurate determination of heart rate during sinus rhythm and cardiac arrhythmia

Han, Dong ; Bashar, Syed Khairul ; Lázaro, Jesús (Universidad de Zaragoza) ; Mohagheghian, Fahimeh ; Peitzsch, Andrew ; Nishita, Nishat ; Ding, Eric ; Dickson, Emily L. ; DiMezza, Danielle ; Scott, Jessica ; Whitcomb, Cody ; Fitzgibbons, Timothy P. ; McManus, David D. ; Chon, Ki H.

Resumen: 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.

Idioma: Inglés

DOI: 10.3390/bios12020082

Año: 2022

Publicado en: Biosensors 12, 2 (2022), 82 [29 pp.]
ISSN: 2079-6374

Factor impacto JCR: 5.4 (2022)

Categ. JCR: CHEMISTRY, ANALYTICAL rank: 14 / 86 = 0.163 (2022) - Q1 - T1
Categ. JCR: INSTRUMENTS & INSTRUMENTATION rank: 11 / 63 = 0.175 (2022) - Q1 - T1
Categ. JCR: NANOSCIENCE & NANOTECHNOLOGY rank: 49 / 107 = 0.458 (2022) - Q2 - T2

Factor impacto CITESCORE: 4.9 - Engineering (Q2) - Physics and Astronomy (Q2) - Chemistry (Q2) - Medicine (Q2) - Biochemistry, Genetics and Molecular Biology (Q2)

Factor impacto SCIMAGO: 0.713 - Instrumentation (Q1) - Biomedical Engineering (Q2) - Medicine (miscellaneous) (Q2) - Clinical Biochemistry (Q2) - Analytical Chemistry (Q2) - Biotechnology (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)