Automated quality metrics based on template matching and matrix profile algorithms accurately classified Laser-Doppler Vibrometry signal quality, achieving at least 80% accuracy and up to 88% accuracy compared to expert visual scoring.
Observational (n=100)
No
Template matching and matrix profile are suitable methods for automated grading of LDV signal quality, achieving ≥80% accuracy compared to expert visual assessment, which may enable future real-time implementation.
Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality. Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed. Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data. Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.
Seoni et al. (Tue,) conducted a observational in Hypertension (n=100). Automated signal quality assessment (template matching and matrix profile) vs. Visual scoring by an expert was evaluated on Accuracy of logistic regression models for signal classification. Automated quality metrics based on template matching and matrix profile algorithms accurately classified Laser-Doppler Vibrometry signal quality, achieving at least 80% accuracy and up to 88% accuracy compared to expert visual scoring.