Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

Blog Article

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, Cutting Boards 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: Entertainment Cards 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.