A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia.

adaptive thresholding atrial fibrillation atrial fibrillation with rapid ventricular response peak correction peak detection photoplethysmograph premature atrial contraction premature ventricle contraction

Journal

Biosensors
ISSN: 2079-6374
Titre abrégé: Biosensors (Basel)
Pays: Switzerland
ID NLM: 101609191

Informations de publication

Date de publication:
29 Jan 2022
Historique:
received: 06 01 2022
revised: 25 01 2022
accepted: 27 01 2022
entrez: 24 2 2022
pubmed: 25 2 2022
medline: 30 3 2022
Statut: epublish

Résumé

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. 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. 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. 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. 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.

Identifiants

pubmed: 35200342
pii: bios12020082
doi: 10.3390/bios12020082
pmc: PMC8869811
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Institute of Health
ID : 1R01 HL137734
Organisme : NSF
ID : 1522087

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Auteurs

Dong Han (D)

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.

Syed Khairul Bashar (SK)

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Jesús Lázaro (J)

BSICoS Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, 50018 Zaragoza, Spain.
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain.

Fahimeh Mohagheghian (F)

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.

Andrew Peitzsch (A)

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.

Nishat Nishita (N)

Department of Public Health Sciences, University of Connecticut Health, Farmington, CT 06030, USA.

Eric Ding (E)

Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.

Emily L Dickson (EL)

College of Osteopathic Medicine, Des Moines University, Des Moines, IA 50312, USA.

Danielle DiMezza (D)

Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.

Jessica Scott (J)

Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.

Cody Whitcomb (C)

School of Medicine, Tufts University, Medford, MA 02155, USA.

Timothy P Fitzgibbons (TP)

Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.

David D McManus (DD)

Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.

Ki H Chon (KH)

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.

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