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Seoni S, Beeckman S, Li Y, Aasmul S, Morbiducci U, Baets R, Boutouyrie P, Molinari F, Madhu N, Segers P. Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals. Front Physiol 2022; 12:775052. [PMID: 35087417 PMCID: PMC8787261 DOI: 10.3389/fphys.2021.775052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/06/2021] [Indexed: 01/21/2023] Open
Abstract
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.
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Affiliation(s)
- Silvia Seoni
- PoliToBIOMed Lab, Biolab, Politecnico di Torino, Turin, Italy
| | - Simeon Beeckman
- IBiTech-bioMMeda, Ghent University, Ghent, Belgium
- IDLab-imec, Ghent University, Ghent, Belgium
| | - Yanlu Li
- Photonics Research Group, Center for Nano- and Biophotonics, Tech Lane Ghent Science Park/Campus A, Ghent University-imec, Ghent, Belgium
| | - Soren Aasmul
- Medtronic Bakken Research Center, Maastricht, Netherlands
| | - Umberto Morbiducci
- Department of Mechanical and Aerospace Engineering, Polytechnic University of Turin, Turin, Italy
| | - Roel Baets
- Photonics Research Group, Center for Nano- and Biophotonics, Tech Lane Ghent Science Park/Campus A, Ghent University-imec, Ghent, Belgium
| | - Pierre Boutouyrie
- INSERM U970, Université de Paris, Assistance Publique Hôpitaux de Paris, Paris, France
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Sirevaag EJ, Casaccia S, Richter EA, O'Sullivan JA, Scalise L, Rohrbaugh JW. Cardiorespiratory interactions: Noncontact assessment using laser Doppler vibrometry. Psychophysiology 2016; 53:847-67. [PMID: 26970208 DOI: 10.1111/psyp.12638] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 01/17/2016] [Indexed: 01/02/2023]
Abstract
The application of a noncontact physiological recording technique, based on the method of laser Doppler vibrometry (LDV), is described. The effectiveness of the LDV method as a physiological recording modality lies in the ability to detect very small movements of the skin, associated with internal mechanophysiological activities. The method is validated for a range of cardiovascular variables, extracted from the contour of the carotid pulse waveform as a function of phase of the respiration cycle. Data were obtained from 32 young healthy participants, while resting and breathing spontaneously. Individual beats were assigned to four segments, corresponding with inspiration and expiration peaks and transitional periods. Measures relating to cardiac and vascular dynamics are shown to agree with the pattern of effects seen in the substantial body of literature based on human and animal experiments, and with selected signals recorded simultaneously with conventional sensors. These effects include changes in heart rate, systolic time intervals, and stroke volume. There was also some evidence for vascular adjustments over the respiration cycle. The effectiveness of custom algorithmic approaches for extracting the key signal features was confirmed. The advantages of the LDV method are discussed in terms of the metrological properties and utility in psychophysiological research. Although used here within a suite of conventional sensors and electrodes, the LDV method can be used on a stand-alone, noncontact basis, with no requirement for skin preparation, and can be used in harsh environments including the MR scanner.
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Affiliation(s)
- Erik J Sirevaag
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Sara Casaccia
- Preston M. Green Department of Electrical and Systems Engineering, School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Ancona, Italy
| | - Edward A Richter
- Preston M. Green Department of Electrical and Systems Engineering, School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Joseph A O'Sullivan
- Preston M. Green Department of Electrical and Systems Engineering, School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Lorenzo Scalise
- Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Ancona, Italy
| | - John W Rohrbaugh
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
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