Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms

Jin, Wen; Chowienczyk, Philip; Alastruey, Jordi

doi:10.1371/journal.pone.0245026

Cited by 26 publications

(18 citation statements)

References 38 publications

(43 reference statements)

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“…In addition, tuning the hyperparameters of the models helps to maximize the performance on the test data for given a specific problem (Elgeldawi et al, 2021). In this project, the hyperparameters optimization of the Machine learning models was made using a random search that has been used in the past for hyperparameter tuning (Jin et al, 2021;Garcia et al, 2022). This algorithm randomly selects different combinations of hyperparameters from a predefined space of values and tests the model's performance model.…”

Section: Model Trainingmentioning

confidence: 99%

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study

Vargas

Bahloul²,

Laleg‐Kirati³

2023

Front. Physiol.

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Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law’s mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R2\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R2\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals.

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Section: Model Trainingmentioning

confidence: 99%

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study

Vargas

Bahloul²,

Laleg‐Kirati³

2023

Front. Physiol.

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“…Similarly, in Tavallali et al [ 6 ], features were extracted from the uncalibrated carotid pulse wave (PW) and clinical information was used to train a NN and estimate PWV. In particular, and focusing on the same objective, in Jin et al [ 7 ] both classic ML and deep learning models were compared using in vivo radial PWs.…”

Section: Introductionmentioning

confidence: 99%

“…Xiao et al [ 15 ] estimated the stroke volume based on features using only radial signals. Jin et al [ 7 ] examined different signal-to-noise ratios and their impact in terms of percentage error by estimating the PWV from radial pressure PW.…”

Section: Introductionmentioning

confidence: 99%

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach

Aguirre

Cymberknop

Grall‐Maës

et al. 2023

Sensors

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Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries.

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“…To increase the accuracy of calculating the velocity distribution, this research proposes the use of machine learning to correct velocity measurements from OFM. Alternatively, inspired by recent developments in data-driven scientific computing, big data, convolutional neural networks (CNN) and deep hidden physics algorithms [ 25 , 26 ], machine learning (ML) has been used in biomedical and bioengineering applications [ 27 , 28 , 29 , 30 , 31 ]. For instance, Raissi et al have developed a physics-informed deep learning method which used passive scalar contours as input and encoded the Navier–Stokes equations into their algorithm [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography

Padhee

Johnson

et al. 2022

Bioengineering

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Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground truth velocity field and projective images of dye flow patterns. The rough velocity field was estimated using the optical flow method (OFM) based on 53 projective images. ML training with least absolute shrinkage, selection operator and convolutional neural network was conducted with CFD velocity data as the ground truth and OFM velocity estimation as the input. The performance of each model was evaluated based on mean absolute error and mean squared error, where all models achieved or surpassed the criteria of 3 × 10−3 and 5 × 10−7 m/s, respectively, with a standard deviation less than 1 × 10−6 m/s. Finally, the interpretable regression and ML models were validated with over 613 image sets. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% on average for the v-velocity estimation in comparison with CFD. The ML framework provided an alternative pathway to support clinical diagnosis by predicting hemodynamic information with high efficiency and accuracy.

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Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms

Cited by 26 publications

References 38 publications

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach

Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography

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