Key result
A classification model using wavelet scattering features of Korotkoff signals detected vascular stiffness with an accuracy of 86.4%, sensitivity of 92.3%, and specificity of 77.8%.
Why the study?
Arterial stiffness independently predicts cardiovascular disease and non-invasive screening methods are limited, prompting exploration of Korotkoff signal characteristics to detect vascular stiffness.
Does a classification model based on wavelet scattering neural network and Korotkoff signals accurately detect vascular stiffness?
Cross-Sectional (n=97)
Does a classification model based on wavelet scattering neural network and Korotkoff signals accurately detect vascular stiffness?
A machine learning model using wavelet scattering networks and LSTM on Korotkoff signals can detect vascular stiffness with 86.4% accuracy, offering a potential new non-invasive screening method.
Answers are grounded in this paper and cite their sources.
May support non-invasive stiffness screening; hypothesis-generating and requires prospective validation before clinical adoption.
Cardiovascular disease is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.
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Ren et al. (2023) conducted a cross-sectional in Vascular stiffness (n=97). Korotkoff signal analysis using wavelet scattering and LSTM networks was evaluated on Classification accuracy for normal vs stiff vessels. A classification model using wavelet scattering features of Korotkoff signals detected vascular stiffness with an accuracy of 86.4%, sensitivity of 92.3%, and specificity of 77.8%.
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