Literature DB >> 28061368

Modeling of the photoplethysmogram during atrial fibrillation.

Andrius Sološenko1, Andrius Petrėnas2, Vaidotas Marozas3, Leif Sörnmo4.   

Abstract

A phenomenological model for simulating the photoplethysmogram (PPG) during atrial fibrillation (AF) is proposed. The simulated PPG is solely based on RR interval information, and, therefore, any annotated ECG database can be used to model sinus rhythm, AF, or rhythms with premature beats. A PPG pulse is modeled by a linear combination of a log-normal and two Gaussian waveforms. The model PPG is obtained by placing individual pulses according to the RR intervals so that a connected signal is created. The model is evaluated on synchronously recorded ECG and PPG signals from the MIMIC and the University of Queensland Vital Signs Dataset databases. The results show that the model PPG signals closely resemble real signal for sinus rhythm, premature beats, as well as for AF. The model is used to study the performance of a low-complexity RR interval-based AF detector on simulated PPG signals with five different pulse types generated using the MIT-BIH AF database at signal-to-noise ratios (SNRs) from 0 to 30dB. PPGs composed of pulses with a dicrotic notch tend to increase the rate of false alarms, especially at lower SNRs. The model is capable of generating simulated PPG signals from RR interval series with sinus rhythm, AF, and premature beats. Considering the lack of annotated, public PPG databases with arrhythmias, the simulation of realistic PPG signals based on annotated ECG signals is expected to facilitate the development and testing of PPG-specific AF detectors.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Arrhythmia; Detection; PPG simulation model

Mesh:

Year:  2016        PMID: 28061368     DOI: 10.1016/j.compbiomed.2016.12.016

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  7 in total

1.  Detection of Common Arrhythmias by the Watch-PAT: Expression of Electrical Arrhythmias by Pulse Recording.

Authors:  Giora Pillar; Murray Berall; Richard B Berry; Tamar Etzioni; Yaakov Henkin; Dennis Hwang; Ibrahim Marai; Faheem Shehadeh; Prasanth Manthena; Anil Rama; Rebecca Spiegel; Thomas Penzel; Riva Tauman
Journal:  Nat Sci Sleep       Date:  2022-04-21

2.  Wearable Photoplethysmography for Cardiovascular Monitoring.

Authors:  Peter H Charlton; Panicos A Kyriaco; Jonathan Mant; Vaidotas Marozas; Phil Chowienczyk; Jordi Alastruey
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2022-03-11       Impact factor: 10.961

Review 3.  Arrhythmia detection and classification using ECG and PPG techniques: a review.

Authors:  H K Sardana; R Kanwade; S Tewary
Journal:  Phys Eng Sci Med       Date:  2021-11-02

Review 4.  A Review of Atrial Fibrillation Detection Methods as a Service.

Authors:  Oliver Faust; Edward J Ciaccio; U Rajendra Acharya
Journal:  Int J Environ Res Public Health       Date:  2020-04-29       Impact factor: 3.390

5.  Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection.

Authors:  Andrius Sološenko; Birutė Paliakaitė; Vaidotas Marozas; Leif Sörnmo
Journal:  Front Physiol       Date:  2022-07-18       Impact factor: 4.755

6.  A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion.

Authors:  Weiguang Ni; Jianzhuo Qi; Lijia Liu; Suyi Li
Journal:  Comput Math Methods Med       Date:  2019-12-30       Impact factor: 2.238

Review 7.  Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review.

Authors:  Malak Abdullah Almarshad; Md Saiful Islam; Saad Al-Ahmadi; Ahmed S BaHammam
Journal:  Healthcare (Basel)       Date:  2022-03-16
  7 in total

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