Literature DB >> 30218408

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

Sibylle Fallet1, Mathieu Lemay2, Philippe Renevey2, Célestin Leupi3, Etienne Pruvot3, Jean-Marc Vesin4.   

Abstract

This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively. Graphical Abstract Atrial fibrillation detection using PPG signals.

Entities:  

Keywords:  Arrhythmias; Atrial fibrillation; Photoplethysmography

Mesh:

Year:  2018        PMID: 30218408     DOI: 10.1007/s11517-018-1886-0

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  13 in total

1.  Permutation entropy: a natural complexity measure for time series.

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3.  Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking.

Authors:  I I Goncharova; J S Barlow
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1990-09

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5.  Heart rate turbulence analysis based on photoplethysmography.

Authors:  Eduardo Gil; Pablo Laguna; Juan Pablo Martínez; Óscar Barquero-Pérez; Arcadi García-Alberola; Leif Sörnmo
Journal:  IEEE Trans Biomed Eng       Date:  2013-06-19       Impact factor: 4.538

6.  Detection of atrial fibrillation episodes using a wristband device.

Authors:  Valentina D A Corino; Rita Laureanti; Lorenzo Ferranti; Giorgio Scarpini; Federico Lombardi; Luca T Mainardi
Journal:  Physiol Meas       Date:  2017-02-02       Impact factor: 2.833

7.  Stochastic complexity measures for physiological signal analysis.

Authors:  I A Rezek; S J Roberts
Journal:  IEEE Trans Biomed Eng       Date:  1998-09       Impact factor: 4.538

8.  Assessment of global atrial fibrillation organization to optimize timing of atrial defibrillation.

Authors:  T H Everett; J R Moorman; L C Kok; J G Akar; D E Haines
Journal:  Circulation       Date:  2001-06-12       Impact factor: 29.690

9.  Detection of atrial fibrillation using an earlobe photoplethysmographic sensor.

Authors:  Thomas Conroy; Jairo Hernandez Guzman; Burr Hall; Gill Tsouri; Jean-Philippe Couderc
Journal:  Physiol Meas       Date:  2017-09-26       Impact factor: 2.833

10.  A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation.

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  7 in total

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Review 2.  Photoplethysmography based atrial fibrillation detection: a review.

Authors:  Tania Pereira; Nate Tran; Kais Gadhoumi; Michele M Pelter; Duc H Do; Randall J Lee; Rene Colorado; Karl Meisel; Xiao Hu
Journal:  NPJ Digit Med       Date:  2020-01-10

3.  A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging.

Authors:  Xiaodong Ding; Yiqin Wang; Yiming Hao; Yi Lv; Rui Chen; Haixia Yan
Journal:  Comput Math Methods Med       Date:  2021-04-01       Impact factor: 2.238

Review 4.  Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review.

Authors:  Linda M Eerikäinen; Alberto G Bonomi; Lukas R C Dekker; Rik Vullings; Ronald M Aarts
Journal:  Cardiovasc Digit Health J       Date:  2020-08-26

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.  Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification.

Authors:  César A Millán; Nathalia A Girón; Diego M Lopez
Journal:  Int J Environ Res Public Health       Date:  2020-01-13       Impact factor: 3.390

7.  Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch.

Authors:  Dong Han; Syed Khairul Bashar; Fahimeh Mohagheghian; Eric Ding; Cody Whitcomb; David D McManus; Ki H Chon
Journal:  Sensors (Basel)       Date:  2020-10-05       Impact factor: 3.847

  7 in total

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