Abstract | ||
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Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis. |
Year | DOI | Venue |
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2020 | 10.3390/s20185362 | SENSORS |
Keywords | DocType | Volume |
laser doppler vibrometry,machine learning,support vector machines,contactless measurements,heartbeat,heart rate detection | Journal | 20 |
Issue | ISSN | Citations |
18 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Luca Antognoli | 1 | 0 | 0.34 |
Sara Moccia | 2 | 38 | 9.44 |
Lucia Migliorelli | 3 | 0 | 1.69 |
Sara Casaccia | 4 | 0 | 0.34 |
Lorenzo Scalise | 5 | 0 | 0.34 |
Emanuele Frontoni | 6 | 248 | 47.04 |