Atrial Fibrillation Detection from Wrist Photoplethysmography Data Using Artificial Neural Networks

Zeinab Rezaei Yousefi, Jakub Parak, Adrian Tarniceriu, Jarkko Harju, Arvi Yli-Hankala, Ilkka Korhonen, Antti Vehkaoja

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    1 Citation (Scopus)
    90 Downloads (Pure)


    Atrial fibrillation (AF) can be detected by analysis of the rhythm of heartbeats. The development of photoplethysmography (PPG) technology has enabled comfortable and unobtrusive physiological monitoring of heart rate with a wrist-worn device. Therefore, it is important to examine the possibility of using PPG signal to detect AF episodes in real-world situations. The aim of this paper is to evaluate an AF detection method based on artificial neural networks (ANN) from PPG-derived beat-to-beat interval data used for primary screening or monitoring purposes. The proposed classifier is able to distinguish between AF and sinus rhythms (SR). In total 30 patients (15 with AF, 15 with SR, mean age 71.5 years) with multiple comorbidities were monitored during routine postoperative treatment. The monitoring included standard ECG and a wrist-worn PPG monitor with green and infrared light sources. The input features of the ANN are based on the information obtained from inter-beat interval (IBI) sequences of 30 consecutive PPG pulses. One of the main concerns about the PPG signals is their susceptibility to be corrupted by noise and artifacts mostly caused by subject movement. Therefore, in the proposed method the IBI reliability is automatically evaluated beforehand. The amount of uncertainty due to unreliable beats was 15.42%. The achieved sensitivity and specificity of AF detection for 30 beats sequences were 99.20 ± 1.3 and 99.54 ± 0.64, respectively. Based on these results, the ANN algorithm demonstrated excellent performance at recognizing AF from SR using wrist PPG data.

    Original languageEnglish
    Title of host publicationWorld Congress on Medical Physics and Biomedical Engineering 2018
    EditorsL Lhotska, L Sukupova, I Lackovic, G Ibbott
    Place of PublicationSingapore
    Number of pages6
    ISBN (Electronic)978-981-10-9038-7
    ISBN (Print)978-981-10-9037-0
    Publication statusPublished - 2019
    Publication typeA4 Article in conference proceedings
    EventWorld Congress on Medical Physics and Biomedical Engineering -
    Duration: 1 Jan 1900 → …

    Publication series

    NameIFMBE Proceedings
    ISSN (Print)1680-0737


    ConferenceWorld Congress on Medical Physics and Biomedical Engineering
    Period1/01/00 → …


    • Aartificial neural network
    • Atrial fibrillation
    • Inter-beat-interval features
    • Photoplethysmography

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Bioengineering
    • Biomedical Engineering


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