Performance of a Medical Smartband with Photoplethysmography Technology and Artificial Intelligence Algorithm to Detect Atrial Fibrillation

Overview

Journal Mhealth

Publisher AME Publishing Company

Date 2025 Feb 13

PMID 39944860

Authors

Sebastiaan Blok

Willem Gielen

Martijn A Piek

Wiert F Hoeksema

Igor Tulevski

G Aernout Somsen

Michiel M Winter

Affiliations

Soon will be listed here.

Abstract

Background: Atrial fibrillation (AF) is a prevalent arrhythmia with significant public health implications, including increased risk of stroke and mortality. Early detection is challenging but crucial for managing complications. Wearable technology with photoplethysmography (PPG) offers a potential solution for long-term, non-invasive monitoring. This study aims to evaluate the performance of three artificial intelligence (AI) algorithms (Happitech, Preventicus, and Philips Biosensing AF) in detecting AF using PPG signals from a medical smartband and compare it with the gold standard electrocardiogram (ECG).

Methods: A medical smartband equipped with PPG technology was used to collect cardiovascular data from patients with and without AF. The sensitivity and specificity of the algorithm for detecting AF were determined by comparing their output to a trained technician's examination of concurrent ECG recordings.

Results: Seventy two participants (42% female, 57±17 years old) were included in this study. The medical smartband provided continuous PPG signals, with AI algorithms evaluating the data for AF episodes. The accuracy of AF detection by the algorithms was compared with that of the concurrent ECG recordings. Sensitivity varied between 80.0% (62.5-97.5%) and 97.6% (97.6-97.6%), specificity between 90.6% (80.5-100%) and 96.9% (90.8-100%).

Conclusions: This study demonstrates the potential of medical smartbands combined with PPG technology and AI algorithms for reliable AF detection. The findings suggest a promising direction for remote AF monitoring and early intervention, potentially reducing AF-related complications and healthcare costs.

References

van Vliet M, Monnink S, Kuiper M, Constandse J, Hoftijzer D, Ronner E . Evaluation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standards. Eur Heart J Digit Health. 2024; 5(3):335-343. PMC: 11104472. DOI: 10.1093/ehjdh/ztae006. View

Zoni-Berisso M, Lercari F, Carazza T, Domenicucci S . Epidemiology of atrial fibrillation: European perspective. Clin Epidemiol. 2014; 6:213-20. PMC: 4064952. DOI: 10.2147/CLEP.S47385. View

Selder J, Te Kolste H, Twisk J, Schijven M, Gielen W, Allaart C . Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study. J Med Internet Res. 2023; 25:e44642. PMC: 10257103. DOI: 10.2196/44642. View

Fitzpatrick T . The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol. 1988; 124(6):869-71. DOI: 10.1001/archderm.124.6.869. View

Yuda E, Shibata M, Ogata Y, Ueda N, Yambe T, Yoshizawa M . Pulse rate variability: a new biomarker, not a surrogate for heart rate variability. J Physiol Anthropol. 2020; 39(1):21. PMC: 7437069. DOI: 10.1186/s40101-020-00233-x. View

Edgar R, Scholte N, Ebrahimkheil K, Brouwer M, Beukema R, Mafi-Rad M . Automated cardiac arrest detection using a photoplethysmography wristband: algorithm development and validation in patients with induced circulatory arrest in the DETECT-1 study. Lancet Digit Health. 2024; 6(3):e201-e210. DOI: 10.1016/S2589-7500(23)00249-2. View

Koenig N, Seeck A, Eckstein J, Mainka A, Huebner T, Voss A . Validation of a New Heart Rate Measurement Algorithm for Fingertip Recording of Video Signals with Smartphones. Telemed J E Health. 2016; 22(8):631-6. DOI: 10.1089/tmj.2015.0212. View

Dur O, Rhoades C, Ng M, Elsayed R, van Mourik R, Majmudar M . Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder. JMIR Mhealth Uhealth. 2018; 6(10):e11040. PMC: 6231731. DOI: 10.2196/11040. View

Krivoshei L, Weber S, Burkard T, Maseli A, Brasier N, Kuhne M . Smart detection of atrial fibrillation†. Europace. 2016; 19(5):753-757. PMC: 5437701. DOI: 10.1093/europace/euw125. View

10.

Nemati S, Ghassemi M, Ambai V, Isakadze N, Levantsevych O, Shah A . Monitoring and detecting atrial fibrillation using wearable technology. Annu Int Conf IEEE Eng Med Biol Soc. 2017; 2016:3394-3397. DOI: 10.1109/EMBC.2016.7591456. View

11.

Selder J, Proesmans T, Breukel L, Dur O, Gielen W, van Rossum A . Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data. Comput Methods Programs Biomed. 2020; 197:105753. DOI: 10.1016/j.cmpb.2020.105753. View

12.

Mol D, Riezebos R, Marquering H, Werner M, Lobban T, de Jong J . Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation. Cardiovasc Digit Health J. 2022; 1(2):107-110. PMC: 8890349. DOI: 10.1016/j.cvdhj.2020.08.004. View

13.

Harmon D, Sehrawat O, Maanja M, Wight J, Noseworthy P . Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation. Arrhythm Electrophysiol Rev. 2023; 12:e12. PMC: 10326669. DOI: 10.15420/aer.2022.31. View

14.

Hochstadt A, Chorin E, Viskin S, Schwartz A, Lubman N, Rosso R . Continuous heart rate monitoring for automatic detection of atrial fibrillation with novel bio-sensing technology. J Electrocardiol. 2018; 52:23-27. DOI: 10.1016/j.jelectrocard.2018.10.096. View

15.

Allen J . Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007; 28(3):R1-39. DOI: 10.1088/0967-3334/28/3/R01. View

16.

Wolf P, Abbott R, Kannel W . Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 1991; 22(8):983-8. DOI: 10.1161/01.str.22.8.983. View

17.

Diederichsen S, Haugan K, Kronborg C, Graff C, Hojberg S, Kober L . Comprehensive Evaluation of Rhythm Monitoring Strategies in Screening for Atrial Fibrillation: Insights From Patients at Risk Monitored Long Term With an Implantable Loop Recorder. Circulation. 2020; 141(19):1510-1522. DOI: 10.1161/CIRCULATIONAHA.119.044407. View

18.

Koerber D, Khan S, Shamsheri T, Kirubarajan A, Mehta S . Accuracy of Heart Rate Measurement with Wrist-Worn Wearable Devices in Various Skin Tones: a Systematic Review. J Racial Ethn Health Disparities. 2022; 10(6):2676-2684. PMC: 9662769. DOI: 10.1007/s40615-022-01446-9. View

19.

Blok S, Piek M, Tulevski I, Somsen G, Winter M . The accuracy of heartbeat detection using photoplethysmography technology in cardiac patients. J Electrocardiol. 2021; 67:148-157. DOI: 10.1016/j.jelectrocard.2021.06.009. View

20.

Stracina T, Ronzhina M, Redina R, Novakova M . Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context. Front Physiol. 2022; 13:867033. PMC: 9082936. DOI: 10.3389/fphys.2022.867033. View