Jean-Philippe Couderc1, Survi Kyal2, Lalit K Mestha2, Beilei Xu2, Derick R Peterson3, Xiaojuan Xia4, Burr Hall4. 1. Heart Research Follow-up Program, Cardiology Department, University of Rochester Medical Center, University of Rochester, New-York. Electronic address: Jean-Philippe.Couderc@heart.rochester.edu. 2. Palo Alto Research Center-A Xerox Company, Webster, New York. 3. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York. 4. Heart Research Follow-up Program, Cardiology Department, University of Rochester Medical Center, University of Rochester, New-York.
Abstract
BACKGROUND: It is estimated that 33.5 million people in the world have developed atrial fibrillation (AF), and an estimated 30% of patients with AF are unaware of their diagnosis (silent AF). OBJECTIVE: The purpose of this study was to test a new technology for contactless detection of AF based on facial video recordings. METHODS: The proposed technique uses a camera to record an individual's face and extract the subtle beat-to-beat variations of skin color reflecting the cardiac pulsatile signal. In a group of adults referred for electrical cardioversion, we recorded the ECG and the video of the subjects' face before and after electrical cardioversion. We extracted the beat-to-beat pulse rates expressed as pulses per minute (ppm) from the videoplethysmographic (VPG) signal acquired using a standard web camera. We introduce a novel quantifier of pulse variability called the pulse harmonic strength (PHS) and report its ability to detect the presence of AF. RESULTS: Eleven subjects (8 male; age 65 ± 6 years) were included in the study. The VPG and ECG-based rates were statistically different between the AF and sinus rhythm periods: 72 ± 9 ppm vs 57 ± 7 ppm (P < .0001) for VPG and 80 ± 17 bpm vs 56 ± 7 bpm (P < .0001) for ECG signals. Among the 407 epochs of 15 seconds of synchronized ECG and VPG signals, PHS was associated with a 20% detection error rate, and the error rates of the automatic ECG-based measurements ranged between 17% and 29%. CONCLUSION: Our preliminary results support the concept that contactless video-based monitoring of the human face for detection of abnormal pulse variability due to AF is feasible.
BACKGROUND: It is estimated that 33.5 million people in the world have developed atrial fibrillation (AF), and an estimated 30% of patients with AF are unaware of their diagnosis (silent AF). OBJECTIVE: The purpose of this study was to test a new technology for contactless detection of AF based on facial video recordings. METHODS: The proposed technique uses a camera to record an individual's face and extract the subtle beat-to-beat variations of skin color reflecting the cardiac pulsatile signal. In a group of adults referred for electrical cardioversion, we recorded the ECG and the video of the subjects' face before and after electrical cardioversion. We extracted the beat-to-beat pulse rates expressed as pulses per minute (ppm) from the videoplethysmographic (VPG) signal acquired using a standard web camera. We introduce a novel quantifier of pulse variability called the pulse harmonic strength (PHS) and report its ability to detect the presence of AF. RESULTS: Eleven subjects (8 male; age 65 ± 6 years) were included in the study. The VPG and ECG-based rates were statistically different between the AF and sinus rhythm periods: 72 ± 9 ppm vs 57 ± 7 ppm (P < .0001) for VPG and 80 ± 17 bpm vs 56 ± 7 bpm (P < .0001) for ECG signals. Among the 407 epochs of 15 seconds of synchronized ECG and VPG signals, PHS was associated with a 20% detection error rate, and the error rates of the automatic ECG-based measurements ranged between 17% and 29%. CONCLUSION: Our preliminary results support the concept that contactless video-based monitoring of the human face for detection of abnormal pulse variability due to AF is feasible.
Authors: Chayakrit Krittanawong; Kipp W Johnson; Robert S Rosenson; Zhen Wang; Mehmet Aydar; Usman Baber; James K Min; W H Wilson Tang; Jonathan L Halperin; Sanjiv M Narayan Journal: Eur Heart J Date: 2019-07-01 Impact factor: 29.983
Authors: Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yu-Feng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg Journal: Circ Arrhythm Electrophysiol Date: 2021-02-12
Authors: Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yufeng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg Journal: Cardiovasc Digit Health J Date: 2021-01-29
Authors: Matthias Daniel Zink; Christoph Brüser; Björn-Ole Stüben; Andreas Napp; Robert Stöhr; Steffen Leonhardt; Nikolaus Marx; Karl Mischke; Jörg B Schulz; Johannes Schiefer Journal: Sci Rep Date: 2017-10-13 Impact factor: 4.379
Authors: Matthias Daniel Zink; Christoph Brüser; Patrick Winnersbach; Andreas Napp; Steffen Leonhardt; Nikolaus Marx; Patrick Schauerte; Karl Mischke Journal: Biomed Res Int Date: 2015-07-01 Impact factor: 3.411