A machine learning approach for computation of cardiovascular intrinsic frequencies.

Autor: Alavi R; Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States of America., Wang Q; Beijing Computational Science Research Center, Beijing, China., Gorji H; Swiss Federal Laboratories for Materials Science and Technology (EMPA), Dubendorf, Switzerland., Pahlevan NM; Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States of America.; Cardiovascular Research Institute, Huntington Medical Research Institutes, Pasadena, CA, United States of America.; Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Oct 26; Vol. 18 (10), pp. e0285228. Date of Electronic Publication: 2023 Oct 26 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0285228
Abstrakt: Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L2 optimization-based method (i.e., R≥0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.
Competing Interests: Niema M. Pahlevan holds equity in Avicena LLC and has a consulting agreement with Avicena LLC. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
(Copyright: © 2023 Alavi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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