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Heartbeat detection by Laser Doppler Vibrometry and Machine Learning
| Content Provider | MDPI |
|---|---|
| Author | Antognoli, Luca Moccia, Sara Migliorelli, Lucia Casaccia, Sara Scalise, Lorenzo Frontoni, Emanuele |
| Copyright Year | 2020 |
| Description | 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 ( |
| Starting Page | 5362 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20185362 |
| Journal | Sensors |
| Issue Number | 18 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-18 |
| Access Restriction | Open |
| Subject Keyword | Sensors Laser Doppler Vibrometry Machine Learning Support Vector Machines Contactless Measurements Heartbeat Heart Rate Detection |
| Content Type | Text |
| Resource Type | Article |
