Instrumentation & Measurement Magazine 24-2 - 58

measurements using two sensors (such as PPG and ECG) and
physical and machine learning models that are used to derive
indices related to arterial stiffness from these signals.
In [21], the spring constant method is applied to the PPG
signal to obtain a coefficient which is shown to be negatively
correlated to cfPWV. The method is based on the fact that the
radial vibration of the peripheral arterial wall can be modeled
using an elastic spring. The spring constant, which represents
the ratio between exerted force and displacement based on
Hooke's law, was assumed to be proportional to the elasticity
of the arteries. This means that lower values of the spring constant represent deterioration of elasticity of an arterial wall.
There have been several works on using machine learning regression models to estimate PWV from tonometry or
PPG signals. This would simplify the measurement of PWV,
because only a single measurement point is required. In [22],
morphological and sub-space features are extracted from the
PPG signal and fed into a regression model based on a neural
network. The standard deviation of the difference between the
actual and predicted PWV was found to be 2.07 m/s. Recently,
the tonometry signal obtained from the carotid artery was used
to estimate PWV with a root mean square error of 1.12 m/s
[23]. The features used for machine learning were extracted
from the carotid arterial pulse waveform, but additional clinical features were added, including blood pressure and age.

An ideal future outcome would be the development of
an accurate automated method/device that can be used routinely by general physicians and/or in the home and that
does not require much training in the same way that automated blood pressure measurement devices have become
part of the routine practice of general medicine. Ideally this
device would be unobtrusive, cuff-less, and accurate. Much
research effort is currently devoted to developing such a
device for continuous measurement of blood pressure; a device that is similarly able to measure arterial stiffness has
the potential to transform the management of cardiovascular health.

References
[1]	 R. R. Townsend et al., " Recommendations for improving and
standardizing vascular research on arterial stiffness: a scientific
statement from the American Heart Association, " Hypertension,
vol. 66, pp. 698-722, 2015.
[2]	 S. S. DeLoach and R. R. Townsend, " Vascular stiffness: its
measurement and significance for epidemiologic and outcome
studies, " Clin. J. Am. Soc. Nephrol., vol. 3, pp. 184-192, 2008.
[3]	 K. Ichikawa, S. Sakuragi, T. Nishihara et al., " Influence of arterial
stiffness on cardiovascular outcome in patients without high
blood pressure, " Heart, vol. 104, pp. 318-323, 2018.
[4]	 S. Laurent et al., " Expert consensus document on arterial stiffness:
methodological issues and clinical applications, " European Heart

Classification

J., vol. 27, pp. 2588-2605, 2006.

Evaluating works on classification in the domain of arterial
stiffness is complex since classes are defined in different ways,
measurement methods are different, and even reference devices used vary significantly. For example, some works (e.g.,
[22]) divide patients into two classes based on whether PWV is
lower or higher than 10 m/s. Other works rely on ultrasound
measurements of the carotid arteries to determine whether
patients have carotid arterial disease or not. PPG, ECG, or
tonometry signals are used, and different morphological features are extracted and fed into classifiers.
Deep learning allows for performing classification without
feature selection. Recent work includes classifying patients
based on the tonometric pulse obtained from the wrist of the
subjects [24]. The pulse is converted to an image and used as an
input to the convolutional neural network. The output of the
network is a soft classifier that classifies patients into one of the
five classes including: hypertension, atherosclerosis, hyperlipidemia, type 2 diabetes, and hypertension complicated by
atherosclerosis. A very high classification accuracy of 95% was
achieved. We expect to see more works that apply deep learning for classifying patient conditions with the trend of creating
larger open-access medical databases.

Conclusion and Future Perspectives
In this article, we presented several current measures used
to quantify arterial stiffness as well as sensors, models, and
methods that have the potential to improve research and development in this field. The review is not comprehensive and
presents only main methods and techniques.
58	

[5]	 K. Hayashi, H. Handa, S. Nagasawa, A. Okumura, K. Moritake,
" Stiffness and elastic behavior of human intracranial and
extracranial arteries, " J. Biomech., vol. 13, pp. 175-84, 1980.
[6]	 L. M. Van Bortel et al., " Expert consensus document on the
measurement of aortic stiffness in daily practice using carotidfemoral pulse wave velocity, " J. Hypertens., vol. 30, pp. 445-448,
2012.
[7]	 V. V. S. Bonarjee, " Arterial stiffness: a prognostic marker
in coronary heart disease. Available methods and clinical
application, " Front. Cardiovasc. Med., vol. 5, no. 64, pp. 1-6, 2018.
[8]	 T. Hirai, S. Sasayama, T. Kawasaki, and S. Yagi, " Stiffness
of systemic arteries in patients with myocardial infarction,
a noninvasive method to predict severity of coronary
atherosclerosis, " Circulation, vol. 80, pp. 78-86, 1989.
[9]	 K. Shirai, J. Utino, K. Otsuka, and M. Takata, " A novel blood
pressure-independent arterial wall stiffness parameter; cardioankle vascular index (CAVI), " J. Atheroscler. Thromb., vol. 13, pp.
101-107, 2006.
[10]	T. Miyoshi and I. Hiroshi, " Assessment of arterial stiffness using
the cardio-ankle vascular index, " Pulse, vol. 4, pp. 11-23, 2016.
[11]	B. Spronck, A. P. Avolio, I. Tan, M. Butlin, K. D. Reesink, and T.
Delhaas, " Arterial stiffness index beta and cardio-ankle vascular
index inherently depend on blood pressure but can be readily
corrected, " J. Hypertens., vol. 35, pp. 98-104, 2017.
[12]	C. Wang et al., " Monitoring of the central blood pressure
waveform via a conformal ultrasonic device, " Nature, Biomedical
Engineering, 2018, accessed Aug. 2020. [Online]. Available: http://
xugroup.eng.ucsd.edu/wp-content/uploads/2018/12/52_Nat_
Biomed_Eng.pdf.

IEEE Instrumentation & Measurement Magazine	

April 2021


http://xugroup.eng.ucsd.edu/wp-content/uploads/2018/12/52_Nat_Biomed_Eng.pdf http://xugroup.eng.ucsd.edu/wp-content/uploads/2018/12/52_Nat_Biomed_Eng.pdf http://xugroup.eng.ucsd.edu/wp-content/uploads/2018/12/52_Nat_Biomed_Eng.pdf

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