Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management_Journal of Biomedical Engineering

Authors：

TAI Meihui ^1,2 , JIN Zhigeng ¹ , WANG Hao ³ ,  GUO Yutao ¹

1. Department of Cardiopulmonary vascular and Thrombotic Diseases, Sixth Medical Department, Chinese PLA General Hospital, Beijing 100048, P. R. China;
2. Chinese PLA Medical College, Beijing 100853, P. R. China;
3. Department of Cardiology, The Second Medical Center, Chinese PLA General Hospital, Beijing 100853, P. R. China;

Corresponding author：

GUO Yutao, Email: dor_guoyt@hotmail.com

Keywords：

Atrial fibrillation; Wearables; Artificial intelligence; Mobile health technology; Photoplethysmography

DOI：

10.7507/1001-5515.202206005

Video：

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Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Early diagnosis and effective management are important to reduce atrial fibrillation‐related adverse events. Photoplethysmography (PPG) is often used to assist wearables for continuous electrocardiograph monitoring, which shows its unique value. The development of PPG has provided an innovative solution to AF management. Serial studies of mobile health technology for improving screening and optimized integrated care in atrial fibrillation have explored the application of PPG in screening, diagnosing, early warning, and integrated management in patients with AF. This review summarizes the latest progress of PPG analysis based on artificial intelligence technology and mobile health in AF field in recent years, as well as the limitations of current research and the focus of future research.

Citation： TAI Meihui, JIN Zhigeng, WANG Hao, GUO Yutao. Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management. Journal of Biomedical Engineering, 2023, 40(6): 1102-1107. doi: 10.7507/1001-5515.202206005 Copy

1.	Zhang J, Johnsen S P, Guo Y, et al. Epidemiology of atrial fibrillation: geographic/ecological risk factors, age, sex, genetics. Cardiac Electrophysiology Clinics, 2021, 13(1): 1-23.
2.	Guo Y, Tian Y, Wang H, et al. Prevalence, incidence, and lifetime risk of atrial fibrillation in China: new insights into the global burden of atrial fibrillation. Chest, 2015, 147(1): 109-119.
3.	Sgreccia D, Manicardi M, Malavasi V L, et al. Comparing outcomes in asymptomatic and symptomatic atrial fibrillation: a systematic review and meta-analysis of 81,462 patients. Journal of Clinical Medicine, 2021, 10(17): 3979.
4.	Burdett P, Lip G Y H. Atrial fibrillation in the UK: predicting costs of an emerging epidemic recognizing and forecasting the cost drivers of atrial fibrillation-related costs. Eur Heart J Qual Care Clin Outcomes, 2022, 8(2): 187-194.
5.	Turakhia M P, Desai M, Hedlin H, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the apple heart study. American Heart Journal, 2019, 207: 66-75.
6.	Steinhubl S R, Waalen J, Edwards A M, et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial. JAMA, 2018, 320(2): 146-155.
7.	Reverberi C, Rabia G, De Rosa F, et al. The RITMIA™ smartphone App for automated detection of atrial fibrillation: accuracy in consecutive patients undergoing elective electrical cardioversion. BioMed Research International, 2019, 2019: 4861951.
8.	Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
9.	Hicks S A, Isaksen J L, Thambawita V, et al. Explaining deep neural networks for knowledge discovery in electrocardiogram analysis. Scientific Reports, 2021, 11(1): 10949.
10.	Zech J R, Badgeley M A, Liu M, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med, 2018, 15(11): e1002683.
11.	Freedman B, Camm J, Calkins H, et al. Screening for atrial fibrillation: a report of the AF-SCREEN international collaboration. Circulation, 2017, 135(19): 1851-1867.
12.	Diederichsen S Z, Haugan K J, Kronborg C, et al. 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.
13.	Fan Y Y, Li Y G, Li J, et al. Diagnostic performance of a smart device with photoplethysmography technology for atrial fibrillation detection: pilot study (Pre-mAFA II registry). JMIR mHealth and uHealth, 2019, 7(3): e11437.
14.	Zhang H, Zhang J, Li H B, et al. Validation of single centre pre-mobile atrial fibrillation Apps for continuous monitoring of atrial fibrillation in a real-world setting: pilot cohort study. Journal of Medical Internet Research, 2019, 21(12): e14909.
15.	Hobbs F D, Fitzmaurice D A, Mant J, et al. A randomised controlled trial and cost-effectiveness study of systematic screening (targeted and total population screening) versus routine practice for the detection of atrial fibrillation in people aged 65 and over. The SAFE study. Health Technology Assessment, 2005, 9(40): iii-iv, ix-x, 1-74.
16.	Halcox J P J, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation, 2017, 136(19): 1784-1794.
17.	Guo Y, Wang H, Zhang H, et al. Mobile Photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
18.	Guo Y, Guo J, Shi X, et al. Mobile health technology-supported atrial fibrillation screening and integrated care: a report from the mAFA-II trial long-term extension cohort. European Journal of Internal Medicine, 2020, 82: 105-111.
19.	Lubitz S A, Faranesh A Z, Selvaggi C, et al. Detection of atrial fibrillation in a large population using wearable devices: the fitbit heart study. Circulation, 2022, 146(19): 1415-1424.
20.	Guo Y, Wang H, Zhang H, et al. Population-based screening or targeted screening based on initial clinical risk assessment for atrial fibrillation: a report from the Huawei heart study. J Clin Med, 2020, 9(5): 1493.
21.	Lowres N, Olivier J, Chao T F, et al. Estimated stroke risk, yield, and number needed to screen for atrial fibrillation detected through single time screening: a multicountry patient-level meta-analysis of 141,220 screened individuals. PLoS Med, 2019, 16(9): e1002903.
22.	Li Y G, Bisson A, Bodin A, et al. C2HEST score and prediction of incident atrial fibrillation in poststroke patients: a French nationwide study. J Am Heart Assoc, 2019, 8(13): e012546.
23.	Lip G Y H, Skjøth F, Nielsen P B, et al. Evaluation of the C2HEST risk score as a possible opportunistic screening tool for incident atrial fibrillation in a healthy population (from a nationwide Danish cohort study). Am J Cardiol, 2020, 125(1): 48-54.
24.	Guo Y, Zhang H, Lip G Y H. Consumer-led screening for atrial fibrillation: a report from the mAFA-II trial long-term extension cohort. JACC Asia. 2022; 2(6): 737-746.
25.	Cai W, Chen Y, Guo J, et al. Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network. Comput Biol Med, 2020, 116: 103378.
26.	Keidar N, Elul Y, Schuster A, et al. Visualizing and quantifying irregular heart rate irregularities to identify atrial fibrillation events. Frontiers in Physiology, 2021, 12: 637680.
27.	Attia Z I, Noseworthy P A, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
28.	Guo Y, Wang H, Zhang H, et al. Photoplethysmography-based machine learning approaches for atrial fibrillation prediction: a report from the huawei heart study. JACC Asia, 2021, 3(1): 399-408.
29.	Hindricks G, Potpara T, Dagres N, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J, 2021, 42(5): 373-498.
30.	Romiti G F, Pastori D, Rivera-Caravaca J M, et al. Adherence to the 'atrial fibrillation better care' pathway in patients with atrial fibrillation: impact on clinical outcomes-a systematic review and meta-analysis of 285,000 patients. Thromb Haemost, 2022, 122(3): 406-414.
31.	Yoon M, Yang P S, Jang E, et al. Improved population-based clinical outcomes of patients with atrial fibrillation by compliance with the simple ABC (atrial fibrillation better care) pathway for integrated care management: a nationwide cohort study. Thromb Haemost, 2019, 119(10): 1695-1703.
32.	Chao T F, Joung B, Takahashi Y, et al. 2021 Focused update consensus guidelines of the Asia Pacific heart rhythm society on stroke prevention in atrial fibrillation: executive summary. Thromb Haemost, 2022, 122(1): 20-47.
33.	Guo Y, Chen Y, Laneda, et al. Mobile health technology for atrial fibrillation management integrating decision support, education, and patient involvement: mAF App trial. Am J Med, 2017, 130(12): 1388-1396.
34.	Guo Y, Lane D A, Wang L, et al. Mobile health (mHealth) technology for improved screening, patient involvement and optimising integrated care in atrial fibrillation: the mAFA (mAF-App) II randomised trial. International Journal of Clinical Practice, 2019, 73(7): e13352.
35.	Ferreira F, Antunes E, Neves R C, et al. INR telemonitoring: efficacy and safety of a telemonitoring program in 453 patients. Acta Med Port, 2012, 25(5): 297-300.
36.	陈汝明, 史展, 卢晓英, 等. 智能手表对心房颤动患者抗凝药依从性的影响. 中华心律失常学杂志, 2022, 26(3): 272-276.
37.	Guo Y, Lane D A, Chen Y, et al. Regular bleeding risk assessment associated with reduction in bleeding outcomes: the mAFA-II randomized trial. The American Journal of Medicine, 2020, 133(10): 1195-1202.
38.	Guo Y, Lane D A, Wang L, et al. Mobile health technology to improve care for patients with atrial fibrillation. J Am Coll Cardiol, 2020, 75(13): 1523-1534.
39.	Proietti M, Marzona I, Vannini T, et al. Long-term relationship between atrial fibrillation, multimorbidity and oral anticoagulant drug use. Mayo Clinic proceedings, 2019, 94(12): 2427-2436.
40.	Ogawa H, An Y, Nishi H, et al. Characteristics and clinical outcomes in atrial fibrillation patients classified using cluster analysis: the Fushimi AF registry. Europace, 2021, 23(9): 1369-1379.
41.	Yao Y, Guo Y, Lip G Y H. The effects of implementing a mobile health-technology supported pathway on atrial fibrillation-related adverse events among patients with multimorbidity: the mAFA-II randomized clinical trial. JAMA Network Open, 2021, 4(12): e2140071.
42.	Luo X, Xu W, Ming W K, et al. Cost-effectiveness of mobile health-based integrated care for atrial fibrillation: model development and data analysis. Journal of Medical Internet Research, 2022, 24(4): e29408.

1. Zhang J, Johnsen S P, Guo Y, et al. Epidemiology of atrial fibrillation: geographic/ecological risk factors, age, sex, genetics. Cardiac Electrophysiology Clinics, 2021, 13(1): 1-23.
2. Guo Y, Tian Y, Wang H, et al. Prevalence, incidence, and lifetime risk of atrial fibrillation in China: new insights into the global burden of atrial fibrillation. Chest, 2015, 147(1): 109-119.
3. Sgreccia D, Manicardi M, Malavasi V L, et al. Comparing outcomes in asymptomatic and symptomatic atrial fibrillation: a systematic review and meta-analysis of 81,462 patients. Journal of Clinical Medicine, 2021, 10(17): 3979.
4. Burdett P, Lip G Y H. Atrial fibrillation in the UK: predicting costs of an emerging epidemic recognizing and forecasting the cost drivers of atrial fibrillation-related costs. Eur Heart J Qual Care Clin Outcomes, 2022, 8(2): 187-194.
5. Turakhia M P, Desai M, Hedlin H, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the apple heart study. American Heart Journal, 2019, 207: 66-75.
6. Steinhubl S R, Waalen J, Edwards A M, et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial. JAMA, 2018, 320(2): 146-155.
7. Reverberi C, Rabia G, De Rosa F, et al. The RITMIA™ smartphone App for automated detection of atrial fibrillation: accuracy in consecutive patients undergoing elective electrical cardioversion. BioMed Research International, 2019, 2019: 4861951.
8. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
9. Hicks S A, Isaksen J L, Thambawita V, et al. Explaining deep neural networks for knowledge discovery in electrocardiogram analysis. Scientific Reports, 2021, 11(1): 10949.
10. Zech J R, Badgeley M A, Liu M, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med, 2018, 15(11): e1002683.
11. Freedman B, Camm J, Calkins H, et al. Screening for atrial fibrillation: a report of the AF-SCREEN international collaboration. Circulation, 2017, 135(19): 1851-1867.
12. Diederichsen S Z, Haugan K J, Kronborg C, et al. 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.
13. Fan Y Y, Li Y G, Li J, et al. Diagnostic performance of a smart device with photoplethysmography technology for atrial fibrillation detection: pilot study (Pre-mAFA II registry). JMIR mHealth and uHealth, 2019, 7(3): e11437.
14. Zhang H, Zhang J, Li H B, et al. Validation of single centre pre-mobile atrial fibrillation Apps for continuous monitoring of atrial fibrillation in a real-world setting: pilot cohort study. Journal of Medical Internet Research, 2019, 21(12): e14909.
15. Hobbs F D, Fitzmaurice D A, Mant J, et al. A randomised controlled trial and cost-effectiveness study of systematic screening (targeted and total population screening) versus routine practice for the detection of atrial fibrillation in people aged 65 and over. The SAFE study. Health Technology Assessment, 2005, 9(40): iii-iv, ix-x, 1-74.
16. Halcox J P J, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation, 2017, 136(19): 1784-1794.
17. Guo Y, Wang H, Zhang H, et al. Mobile Photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
18. Guo Y, Guo J, Shi X, et al. Mobile health technology-supported atrial fibrillation screening and integrated care: a report from the mAFA-II trial long-term extension cohort. European Journal of Internal Medicine, 2020, 82: 105-111.
19. Lubitz S A, Faranesh A Z, Selvaggi C, et al. Detection of atrial fibrillation in a large population using wearable devices: the fitbit heart study. Circulation, 2022, 146(19): 1415-1424.
20. Guo Y, Wang H, Zhang H, et al. Population-based screening or targeted screening based on initial clinical risk assessment for atrial fibrillation: a report from the Huawei heart study. J Clin Med, 2020, 9(5): 1493.
21. Lowres N, Olivier J, Chao T F, et al. Estimated stroke risk, yield, and number needed to screen for atrial fibrillation detected through single time screening: a multicountry patient-level meta-analysis of 141,220 screened individuals. PLoS Med, 2019, 16(9): e1002903.
22. Li Y G, Bisson A, Bodin A, et al. C2HEST score and prediction of incident atrial fibrillation in poststroke patients: a French nationwide study. J Am Heart Assoc, 2019, 8(13): e012546.
23. Lip G Y H, Skjøth F, Nielsen P B, et al. Evaluation of the C2HEST risk score as a possible opportunistic screening tool for incident atrial fibrillation in a healthy population (from a nationwide Danish cohort study). Am J Cardiol, 2020, 125(1): 48-54.
24. Guo Y, Zhang H, Lip G Y H. Consumer-led screening for atrial fibrillation: a report from the mAFA-II trial long-term extension cohort. JACC Asia. 2022; 2(6): 737-746.
25. Cai W, Chen Y, Guo J, et al. Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network. Comput Biol Med, 2020, 116: 103378.
26. Keidar N, Elul Y, Schuster A, et al. Visualizing and quantifying irregular heart rate irregularities to identify atrial fibrillation events. Frontiers in Physiology, 2021, 12: 637680.
27. Attia Z I, Noseworthy P A, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
28. Guo Y, Wang H, Zhang H, et al. Photoplethysmography-based machine learning approaches for atrial fibrillation prediction: a report from the huawei heart study. JACC Asia, 2021, 3(1): 399-408.
29. Hindricks G, Potpara T, Dagres N, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J, 2021, 42(5): 373-498.
30. Romiti G F, Pastori D, Rivera-Caravaca J M, et al. Adherence to the 'atrial fibrillation better care' pathway in patients with atrial fibrillation: impact on clinical outcomes-a systematic review and meta-analysis of 285,000 patients. Thromb Haemost, 2022, 122(3): 406-414.
31. Yoon M, Yang P S, Jang E, et al. Improved population-based clinical outcomes of patients with atrial fibrillation by compliance with the simple ABC (atrial fibrillation better care) pathway for integrated care management: a nationwide cohort study. Thromb Haemost, 2019, 119(10): 1695-1703.
32. Chao T F, Joung B, Takahashi Y, et al. 2021 Focused update consensus guidelines of the Asia Pacific heart rhythm society on stroke prevention in atrial fibrillation: executive summary. Thromb Haemost, 2022, 122(1): 20-47.
33. Guo Y, Chen Y, Laneda, et al. Mobile health technology for atrial fibrillation management integrating decision support, education, and patient involvement: mAF App trial. Am J Med, 2017, 130(12): 1388-1396.
34. Guo Y, Lane D A, Wang L, et al. Mobile health (mHealth) technology for improved screening, patient involvement and optimising integrated care in atrial fibrillation: the mAFA (mAF-App) II randomised trial. International Journal of Clinical Practice, 2019, 73(7): e13352.
35. Ferreira F, Antunes E, Neves R C, et al. INR telemonitoring: efficacy and safety of a telemonitoring program in 453 patients. Acta Med Port, 2012, 25(5): 297-300.
36. 陈汝明, 史展, 卢晓英, 等. 智能手表对心房颤动患者抗凝药依从性的影响. 中华心律失常学杂志, 2022, 26(3): 272-276.
37. Guo Y, Lane D A, Chen Y, et al. Regular bleeding risk assessment associated with reduction in bleeding outcomes: the mAFA-II randomized trial. The American Journal of Medicine, 2020, 133(10): 1195-1202.
38. Guo Y, Lane D A, Wang L, et al. Mobile health technology to improve care for patients with atrial fibrillation. J Am Coll Cardiol, 2020, 75(13): 1523-1534.
39. Proietti M, Marzona I, Vannini T, et al. Long-term relationship between atrial fibrillation, multimorbidity and oral anticoagulant drug use. Mayo Clinic proceedings, 2019, 94(12): 2427-2436.
40. Ogawa H, An Y, Nishi H, et al. Characteristics and clinical outcomes in atrial fibrillation patients classified using cluster analysis: the Fushimi AF registry. Europace, 2021, 23(9): 1369-1379.
41. Yao Y, Guo Y, Lip G Y H. The effects of implementing a mobile health-technology supported pathway on atrial fibrillation-related adverse events among patients with multimorbidity: the mAFA-II randomized clinical trial. JAMA Network Open, 2021, 4(12): e2140071.
42. Luo X, Xu W, Ming W K, et al. Cost-effectiveness of mobile health-based integrated care for atrial fibrillation: model development and data analysis. Journal of Medical Internet Research, 2022, 24(4): e29408.

Journal of Biomedical Engineering

Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management

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