Wrist-located optical device for atrial fibrillation screening: A clinical study on twenty patients

This study aims at evaluating the performances of a wrist-located device to detect atrial fibrillation (AF) based on photoplethysmography (PPG) technology. Twenty patients referred for catheter ablation of cardiac arrhythmias in whom episodes of sinus rhythm (SR) and AF coexisted were screened. Screening included a 12-lead electrocardiogram (ECG) and a PPG device placed at the wrist measuring cardiac pulsatility by means of infrared light. While reference cardiac interbeat (RR) intervals were obtained from the analysis of the ECG signals, RR intervals from the PPG signals were estimated by detecting systolic down-strokes on the optical waveforms. Classification of SR versus AF epochs was obtained via a support vector machine to which features extracted on 10-second windows were provided. Extracted features included mean, standard deviation, minimum, and interquartile range of RR within an epoch. A total number of 2213 epochs (1927 of AF, 286 of SR) were analyzed, providing classification accuracy of 93.85% for the PPG-based classifier and 98.93% for the ECG-based classifier. These preliminary results suggest that a wrist-located PPG-based monitor might be eligible for future screening of AF in large populations.

[1]  Michael R. Neuman,et al.  Wearable sensors : fundamentals, implementation and applications , 2014 .

[2]  D. Singer,et al.  Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. , 2001, JAMA.

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  J. Sola,et al.  Portable SpO2 Monitor: A Fast Response Approach , 2007, 2007 IEEE International Conference on Portable Information Devices.

[5]  Jean-Marc Vesin,et al.  An adaptive organization index to characterize atrial fibrillation using wrist-type photoplethysmographic signals , 2016, 2016 Computing in Cardiology Conference (CinC).

[6]  R. Anderson,et al.  The optics of human skin. , 1981, The Journal of investigative dermatology.

[7]  Neil Sulke,et al.  Are implantable cardiac monitors the 'gold standard' for atrial fibrillation detection? A prospective randomized trial comparing atrial fibrillation monitoring using implantable cardiac monitors and DDDRP permanent pacemakers in post atrial fibrillation ablation patients. , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[8]  John Allen.  Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

[9]  David J Callans.  I left my smartphone at home and can't tell if I'm in atrial fibrillation. , 2013, Heart rhythm.

[10]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[11]  R. Stewart,et al.  Influence of heart rate on stroke volume variability in atrial fibrillation in patients with normal and impaired left ventricular function. , 1998, The American journal of cardiology.

[12]  Ki H. Chon,et al.  A Novel Approach to Monitor Nonstationary Dynamics in Physiological Signals: Application to Blood Pressure, Pulse Oximeter, and Respiratory Data , 2010, Annals of Biomedical Engineering.

[13]  J. Alpert,et al.  ACC/AHA guidelines for ambulatory electrocardiography: executive summary and recommendations. A report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to revise the guidelines for ambulatory electrocardiography). , 1999, Circulation.

[14]  Mathieu Lemay,et al.  Photoplethysmography-based ambulatory heartbeat monitoring embedded into a dedicated bracelet , 2013, Computing in Cardiology 2013.