OBJECTIVE: This study proposes an algorithm for the detection of atrial fibrillation (AF), designed to operate on extended photoplethysmographic (PPG) signals recorded using a wrist-worn device of own design. APPROACH: Robustness against false alarms is achieved by means of signal quality assessment and different techniques for suppression of ectopic beats, bigeminy, and respiratory sinus arrhythmia. The decision logic is based on our previously proposed RR interval-based AF detector, but modified to account for differences between interbeat intervals in the ECG and the PPG. The detector is evaluated on simulated PPG signals as well as on clinical PPG signals recorded during cardiac rehabilitation after myocardial infarction. MAIN RESULTS: Analysis of the clinical signals showed that 1.5 false alarms were on average produced per day with a sensitivity of 72.0% and a specificity of 99.7% when 89.2% of the database was available for analysis, whereas as many as 15 when the RR interval-based AF detector, boosted by accelerometer information for signal quality assessment, was used. However, a sensitivity of 97.2% and a specificity of 99.6% were achieved when increasing the demands on signal quality so that 50% was available for analysis. SIGNIFICANCE: The proposed detector offers promising performance and is particularly well-suited for implementation in low-power wearable devices, e.g. wrist-worn devices, with significance in mass screening applications.
OBJECTIVE: This study proposes an algorithm for the detection of atrial fibrillation (AF), designed to operate on extended photoplethysmographic (PPG) signals recorded using a wrist-worn device of own design. APPROACH: Robustness against false alarms is achieved by means of signal quality assessment and different techniques for suppression of ectopic beats, bigeminy, and respiratory sinus arrhythmia. The decision logic is based on our previously proposed RR interval-based AF detector, but modified to account for differences between interbeat intervals in the ECG and the PPG. The detector is evaluated on simulated PPG signals as well as on clinical PPG signals recorded during cardiac rehabilitation after myocardial infarction. MAIN RESULTS: Analysis of the clinical signals showed that 1.5 false alarms were on average produced per day with a sensitivity of 72.0% and a specificity of 99.7% when 89.2% of the database was available for analysis, whereas as many as 15 when the RR interval-based AF detector, boosted by accelerometer information for signal quality assessment, was used. However, a sensitivity of 97.2% and a specificity of 99.6% were achieved when increasing the demands on signal quality so that 50% was available for analysis. SIGNIFICANCE: The proposed detector offers promising performance and is particularly well-suited for implementation in low-power wearable devices, e.g. wrist-worn devices, with significance in mass screening applications.
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
Authors: Craig J Goergen; MacKenzie J Tweardy; Steven R Steinhubl; Stephan W Wegerich; Karnika Singh; Rebecca J Mieloszyk; Jessilyn Dunn Journal: Annu Rev Biomed Eng Date: 2021-12-21 Impact factor: 11.324
Authors: Dong Han; Syed K Bashar; Fearass Zieneddin; Eric Ding; Cody Whitcomb; David D McManus; Ki H Chon Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2020-07
Authors: Eemu-Samuli Väliaho; Jukka A Lipponen; Pekka Kuoppa; Tero J Martikainen; Helena Jäntti; Tuomas T Rissanen; Maaret Castrén; Jari Halonen; Mika P Tarvainen; Tiina M Laitinen; Tomi P Laitinen; Onni E Santala; Olli Rantula; Noora S Naukkarinen; Juha E K Hartikainen Journal: Front Physiol Date: 2022-01-04 Impact factor: 4.566