000109670 001__ 109670
000109670 005__ 20240319080948.0
000109670 0247_ $$2doi$$a10.3390/bios12020082
000109670 0248_ $$2sideral$$a127620
000109670 037__ $$aART-2022-127620
000109670 041__ $$aeng
000109670 100__ $$aHan, Dong
000109670 245__ $$aA real-time ppg peak detection method for accurate determination of heart rate during sinus rhythm and cardiac arrhythmia
000109670 260__ $$c2022
000109670 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109670 5203_ $$aObjective: 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.
000109670 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS
000109670 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109670 590__ $$a5.4$$b2022
000109670 592__ $$a0.713$$b2022
000109670 591__ $$aCHEMISTRY, ANALYTICAL$$b14 / 86 = 0.163$$c2022$$dQ1$$eT1
000109670 593__ $$aInstrumentation$$c2022$$dQ1
000109670 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b11 / 63 = 0.175$$c2022$$dQ1$$eT1
000109670 593__ $$aBiomedical Engineering$$c2022$$dQ2
000109670 591__ $$aNANOSCIENCE & NANOTECHNOLOGY$$b49 / 107 = 0.458$$c2022$$dQ2$$eT2
000109670 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2
000109670 593__ $$aClinical Biochemistry$$c2022$$dQ2
000109670 593__ $$aAnalytical Chemistry$$c2022$$dQ2
000109670 593__ $$aBiotechnology$$c2022$$dQ2
000109670 594__ $$a4.9$$b2022
000109670 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109670 700__ $$aBashar, Syed Khairul
000109670 700__ $$0(orcid)0000-0001-8742-0072$$aLázaro, Jesús$$uUniversidad de Zaragoza
000109670 700__ $$aMohagheghian, Fahimeh
000109670 700__ $$aPeitzsch, Andrew
000109670 700__ $$aNishita, Nishat
000109670 700__ $$aDing, Eric
000109670 700__ $$aDickson, Emily L.
000109670 700__ $$aDiMezza, Danielle
000109670 700__ $$aScott, Jessica
000109670 700__ $$aWhitcomb, Cody
000109670 700__ $$aFitzgibbons, Timothy P.
000109670 700__ $$aMcManus, David D.
000109670 700__ $$aChon, Ki H.
000109670 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000109670 773__ $$g12, 2 (2022), 82 [29 pp.]$$tBiosensors$$x2079-6374
000109670 8564_ $$s477149$$uhttps://zaguan.unizar.es/record/109670/files/texto_completo.pdf$$yVersión publicada
000109670 8564_ $$s2728267$$uhttps://zaguan.unizar.es/record/109670/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109670 909CO $$ooai:zaguan.unizar.es:109670$$particulos$$pdriver
000109670 951__ $$a2024-03-18-12:49:49
000109670 980__ $$aARTICLE