| Title |
Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?
|
|---|---|
| Published in |
Medical & Biological Engineering & Computing, September 2018
|
| DOI | 10.1007/s11517-018-1886-0 |
| Pubmed ID | |
| Authors |
Sibylle Fallet, Mathieu Lemay, Philippe Renevey, Célestin Leupi, Etienne Pruvot, Jean-Marc Vesin |
| Abstract |
This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively. Graphical Abstract Atrial fibrillation detection using PPG signals. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 18% |
| Student > Doctoral Student | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Student > Ph. D. Student | 4 | 9% |
| Student > Master | 3 | 7% |
| Other | 5 | 11% |
| Unknown | 15 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 27% |
| Medicine and Dentistry | 9 | 20% |
| Computer Science | 6 | 14% |
| Sports and Recreations | 1 | 2% |
| Neuroscience | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 14 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2021.
All research outputs
#7,963,683
of 25,385,509 outputs
Outputs from Medical & Biological Engineering & Computing
#522
of 2,053 outputs
Outputs of similar age
#129,994
of 348,472 outputs
Outputs of similar age from Medical & Biological Engineering & Computing
#3
of 16 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 2,053 research outputs from this source. They receive a mean Attention Score of 3.8. This one has gotten more attention than average, scoring higher than 73% of its peers.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.