Objective: The aim of this study was to assess the relationship between ankle brachial pressure index and pulse wave velocity in patients with peripheral arterial disease.
Methods: Brachial-knee and brachial-ankle pulse wave velocity were measured by pneumoplethysmography using cuffs in a standard technique. Correlation between pulse wave velocity and Doppler-ankle brachial pressure index was assessed by Spearman correlation and receiver operating curves.
Results: A total of 133 claudicants were assessed. Analysis by developing receiver operating curves for ankle brachial pressure index and pulse wave velocity showed that patients with ankle brachial pressure index over 0.6 were more likely to have a bk-pulse wave velocity over 9.2 m/s and ba-pulse wave velocity over 9.5 m/s.
Conclusion: These results show for the first time that nondiabetic vascular patients may have measurable significant arterial stiffness independent of an impaired ankle brachial pressure index. These data suggest that pulse wave velocity may be valuable in screening and evaluating the severity of peripheral arterial disease.
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http://dx.doi.org/10.1177/0003319708329335 | DOI Listing |
Am J Kidney Dis
January 2025
Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address:
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Neurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK.
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Federal State Budgetary Institution , Technological Institute for Superhard and Novel Carbon Materials of National Research Centre, Kurchatov Institute, 108840 Moscow, Troitsk, Russian Federation.
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College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on esNet ong Short-Term Memory with an ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range-Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal.
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