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Noninvasive blood pressure and the second heart sound analysis
Insights
This study analyzed children's heart sounds to estimate blood pressure. Frequency-based features of the second heart sound (S2) showed the best accuracy in predicting mean blood pressure noninvasively.
Area of Science:
- Cardiology
- Biomedical Engineering
- Pediatric Health
Background:
- Heart sound characteristics correlate with blood pressure, crucial for cardiovascular disease detection.
- Accurate blood pressure monitoring in children is vital for early disease identification and management.
Purpose of the Study:
- To automatically segment heart sounds (S1 and S2) from pediatric patients.
- To extract time, frequency, and wavelet-based features from the second heart sound (S2).
- To analyze the relationship between S2 features and noninvasive blood pressure measurements in children.
Main Methods:
- Acquisition and automatic segmentation of heart sound auscultations from 27 pediatric patients.
- Extraction of diverse features (time, frequency, wavelet) from the S2 component.
- Multivariate regression analysis to correlate S2 features with cuff-based mean blood pressure measurements.
Main Results:
- Frequency-based features of S2 demonstrated the strongest correlation with mean blood pressure.
- The best performance achieved a Mean Absolute Error (MAE) of 6.08 mmHg and Mean Absolute Percentage Error (MAPE) of 7.85% in estimating mean blood pressure.
- Leave-one-out cross-validation confirmed the robustness of the frequency features for blood pressure estimation.
Conclusions:
- Frequency analysis of the second heart sound (S2) offers a promising noninvasive method for estimating blood pressure in children.
- This approach could lead to improved cardiovascular disease screening and monitoring in pediatric populations.
- Further research can refine these methods for broader clinical application in pediatric cardiology.
Abstract:
Heart sound characteristics are linked to blood pressure, and its interpretation is important for detection of cardiovascular disease. In this study, heart sounds' auscultation, acquired from children patients (27 patients, 10.2±3.9 years, 35.7±20.8 kg, 132.3±25.5 cm), were automatically segmented to extract the two main components: the first sound (S1) and the second sound (S2). Following, a set of time, frequency, and wavelet based features, were extracted from the S2, and analyzed in relation to the noninvasive cuff-based measures of blood pressure (mean blood pressure of 78±8.8 mmHg). A multivariate regression analysis was performed for each S2 feature set to determine which features better related to the blood pressure measurements. The best results, in the leave-one-out evaluation, were obtained using the frequency features set, with a MAE of 6.08 mmHg, a MAPE of 7.85%, and a ME of 0.31 mmHg, in the estimation of the mean blood pressure.
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