Spectral analysis of bruits with an electronic stethoscope enhances screening of carotid stenosis and plaques beyond conventional auscultation

Figures

• 

  Figure 1. Steps followed for processing and evaluating electronic stethoscope (e-stethoscope) recordings, including pre-processing, feature extraction, predictive model training, and performance evaluation. FNR, false negative rate; FPR, false positive rate; ROC, receiver operating characteristic; TNR, true negative rate; TPR, true positive rate.

• 

  Figure 2. Flowchart of study population and vessel case selection.

• 

  Figure 3. Paired duplex ultrasound and e-stethoscope recordings for patients with no plaque (left panels) and severe stenosis (right panels). The upper panels show B-mode and Doppler ultrasound spectra, with time on the x-axis and velocity on the y-axis. The lower panels present the time-frequency distribution from the e-stethoscope, where time is on the x-axis and frequency is on the y-axis. In the case of severe stenosis, the e-stethoscope distribution reveals a distinct high-frequency systolic bruit, corresponding to the broader Doppler spectrum and elevated peak systolic frequency. EDV, end diastolic velocity; ICA, internal carotid artery; PSV, peak systolic velocity.

• 

  Figure 4. Sensitivity and specificity outcomes using varying weights in the Youden index formula JW=2×[w×sensitivity+(1−w)×specificity]−1 to determine the optimal cut-off point for stenosis detection. Results are shown for distal (A) and proximal (B) location recordings. The x-axis indicates the weight assigned to sensitivity relative to specificity.

• 

  Figure 5. Overview showing the relationship between individual features and the degree of stenosis based on recordings at the distal location. Dots represent median values, while bars indicate interquartile ranges. a.u., arbitrary unit; IFI, intensity of flow instabilities; IMF, intrinsic mode function.

• 

  Figure 6. ROC curves for detecting severe stenosis and plaques using all proposed features as inputs to binary logistic regression models during testing. Details on the confidence intervals for the area under the ROC curves are provided in Table S1. AUC, area under the curve; ROC, receiver operating characteristic.

• 

  Figure 7. Contingency tables showing the training and testing performance for detecting severe stenosis and plaques, based on recordings from distal and proximal locations. These tables were created using the optimal cut-off point, determined by maximizing the weighted Youden index. Details on the confidence intervals for the performance metrics are provided in Table S1. FNR, false negative rate; FPR, false positive rate; TNR, true negative rate; TPR, true positive rate.

• 

  Figure 8. Overview showing the relationship between individual features and the degree of stenosis based on recordings at the proximal location. Dots represent median values, and bars indicate interquartile ranges. a.u., arbitrary unit; IFI, intensity of flow instabilities; IMF, intrinsic mode function.