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Analysis of blood pressure signal in patients with different ventricular ejection fraction using linear and
Insights
Cardiomyopathy patients were analyzed using blood pressure signals to assess risk. Combining linear and nonlinear analysis of systolic time intervals and pulse pressure improved patient risk stratification.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Left ventricle dysfunction in cardiomyopathy affects cardiovascular hemodynamics and electrical activity.
- Patients with cardiomyopathy are typically stratified into low-risk (LVEF>35%) and high-risk (LVEF≤35%) groups based on left ventricular ejection fraction (LVEF).
Purpose of the Study:
- To characterize cardiomyopathy patients using linear and nonlinear methods applied to blood pressure (BP) signals.
- To evaluate the effectiveness of combining linear and nonlinear features for improved risk stratification.
Main Methods:
- Analyzed BP signals from 49 cardiomyopathy patients, categorized by LVEF into low-risk and high-risk groups.
- Extracted systolic time interval (STI), upward systolic slope (BPsl), and pulse pressure (PP) from BP signals.
- Employed spectral estimation (linear) and recurrence plot (nonlinear) analyses, utilizing sequential feature selection (SFS) to identify optimal parameters.
Main Results:
- The best patient classification was achieved using a combination of linear and nonlinear features from STI and PP.
- Optimal classification involved spectral frequency peaks and recurrence plot diagonal structures for STI, yielding an AUC of 79%.
- Similar classification performance was observed when analyzing PP values.
Conclusions:
- Combining linear and nonlinear parameters derived from BP signal analysis can enhance the risk stratification of cardiomyopathy patients.
- This integrated approach offers a promising method for more accurate patient assessment and management.
Abstract:
Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.
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Monitoring Both Arms:
Monitoring BP in both arms during the initial assessment is advisable, as the systolic value may differ by five to ten mm Hg between arms. For subsequent BP assessments, use the arm with the higher reading.
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Preparation of Equipment:
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