Markus R Mutke1,2, Noe Brasier1,3, Christina Raichle4, Flavia Ravanelli1, Marcus Doerr5,6, Jens Eckstein1,2. 1. Chief Medical Information Officer - Office, University Hospital Basel, Basel, Switzerland. 2. Department of Internal Medicine, University Hospital Basel, Basel, Switzerland. 3. Department of Internal Medicine, Kantonsspital Obwalden, Sarnen, Switzerland. 4. Department of Gastroenterology, Internal Medicine, Kantonsspital Basel-Land, Switzerland. 5. German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany. 6. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.
Abstract
Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, can be detected by smartphones and smartwatches. Introduction: Single-lead ECGs (iECGs) and photoplethysmography (PPG) sensors provide the opportunity for a broad, simple, and easily repeatable cardiac rhythm analysis. To reduce unnecessary medical follow-up testing due to false positive results, our aim was to find a screening approach applicable on smart devices with a focus on high specificity. Methods: We used PPG measurements from smartphones and smartwatches and iECG data from two previous validation trials. Two AF detection algorithms (A and B) were applied on the iECG dataset and compared directly. Further, we used 1-min PPG measurements as a first-pass filter for arrhythmia detection and simulated a sequential testing: Once an arrhythmia was detected in the PPG, the iECG counterpart of the patient was analyzed by algorithm A, B, or A + B combined although algorithm B was primarily designed for PPG analysis. Results: The iECGs from 1,288 participants were analyzed. Algorithm A did not show a diagnosis in 16.1%. In the remaining, sensitivity and specificity were 99.6%, and 97.4% respectively. Accuracy was 98.5%, and correct classification rate (CCR) was 82.7%. Algorithm B always differentiated between normal and arrhythmic and reached an overall sensitivity of 95.4%, a specificity of 91.6%, and an accuracy and CCR of 93.3%. Sequential testing by combining both algorithms into a three-phase test (Test positive PPG, then iECG analysis by A and B combined) resulted in a 100% specificity. Conclusion: Algorithm B performed strongly in PPG analysis as well as iECG analysis. PPG signals and consecutive iECG combined when an arrhythmia was detected by PPG resulted in a specificity that was higher than 99%. Discussion: The analysis allows a direct comparison of iECG algorithms without possible dilution by different measurement procedures or recording-devices. We improved specificity in AF-screening approaches with wearables by simulating a novel approach. Results rely on signal quality.
Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, can be detected by smartphones and smartwatches. Introduction: Single-lead ECGs (iECGs) and photoplethysmography (PPG) sensors provide the opportunity for a broad, simple, and easily repeatable cardiac rhythm analysis. To reduce unnecessary medical follow-up testing due to false positive results, our aim was to find a screening approach applicable on smart devices with a focus on high specificity. Methods: We used PPG measurements from smartphones and smartwatches and iECG data from two previous validation trials. Two AF detection algorithms (A and B) were applied on the iECG dataset and compared directly. Further, we used 1-min PPG measurements as a first-pass filter for arrhythmia detection and simulated a sequential testing: Once an arrhythmia was detected in the PPG, the iECG counterpart of the patient was analyzed by algorithm A, B, or A + B combined although algorithm B was primarily designed for PPG analysis. Results: The iECGs from 1,288 participants were analyzed. Algorithm A did not show a diagnosis in 16.1%. In the remaining, sensitivity and specificity were 99.6%, and 97.4% respectively. Accuracy was 98.5%, and correct classification rate (CCR) was 82.7%. Algorithm B always differentiated between normal and arrhythmic and reached an overall sensitivity of 95.4%, a specificity of 91.6%, and an accuracy and CCR of 93.3%. Sequential testing by combining both algorithms into a three-phase test (Test positive PPG, then iECG analysis by A and B combined) resulted in a 100% specificity. Conclusion: Algorithm B performed strongly in PPG analysis as well as iECG analysis. PPG signals and consecutive iECG combined when an arrhythmia was detected by PPG resulted in a specificity that was higher than 99%. Discussion: The analysis allows a direct comparison of iECG algorithms without possible dilution by different measurement procedures or recording-devices. We improved specificity in AF-screening approaches with wearables by simulating a novel approach. Results rely on signal quality.
Authors: Astrid N L Hermans; Monika Gawalko; Lisa Dohmen; Rachel M J van der Velden; Konstanze Betz; David Duncker; Dominique V M Verhaert; Hein Heidbuchel; Emma Svennberg; Lis Neubeck; Jens Eckstein; Deirdre A Lane; Gregory Y H Lip; Harry J G M Crijns; Prashanthan Sanders; Jeroen M Hendriks; Nikki A H A Pluymaekers; Dominik Linz Journal: Clin Res Cardiol Date: 2021-09-21 Impact factor: 6.138
Authors: Larissa Fabritz; D Connolly; E Czarnecki; D Dudek; A Zlahoda-Huzior; E Guasch; D Haase; T Huebner; K Jolly; P Kirchhof; Ulrich Schotten; Antonia Zapf; Renate B Schnabel Journal: Front Cardiovasc Med Date: 2022-03-21