Cardiovascular Disease Probability Regression Model
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Solution Overview
Problem
Traditional methods for diagnosing cardiovascular diseases using cardiovascular sounds are limited in detecting sounds outside the human auditory range or obscured by normal physiological sounds, leading to weaker discriminatory power and a need for improved prediction and probability determination of cardiovascular disease.
Innovation Solution
A probability regression model is employed to generate parameter estimates using disease status information and predictors derived from cardiovascular sound signals, enabling the prediction of cardiovascular disease probability through a system that includes clinical data and sound signal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clinical auscultation is used to detect cardiovascular sounds, then the method poses minimal risk to the subject, but it cannot detect sounds outside the normal human auditory range or those obscured by normal physiological sounds
Solution Approach 1:
The patent replaces traditional mechanical auscultation with electronic signal processing systems. Digital stethoscopes convert acoustic signals to electrical signals, enabling processing beyond human auditory capabilities. Signal processing algorithms then analyze these electrical signals to detect abnormal cardiovascular sounds that would be imperceptible to human ears, thereby substituting mechanical/biological detection with electronic and computational methods.
Solution Approach 2:
The patent introduces digital signal processing as an intermediary between the cardiovascular sounds and the diagnostic interpretation. The system uses intermediate representations such as spectrograms, wavelet transforms, and extracted features to bridge the gap between raw acoustic signals and disease diagnosis, enabling detection of subtle abnormalities that direct human listening cannot perceive.
2Measurement precision
If digital stethoscope devices with signal-processing techniques are used to identify features of interest, then the ability to detect abnormal sounds improves, but the discriminatory power remains weaker than desired
Solution Approach 1:
The patent applies parameter changes by transforming cardiovascular sound signals into different feature spaces through various signal processing techniques. It extracts multiple parameters including time-domain features, frequency-domain features, and time-frequency features. By changing the representation parameters of the signals and selecting the most discriminative parameters, the system enhances its ability to distinguish between normal and abnormal cardiovascular sounds.
Solution Approach 2:
The patent transitions from one-dimensional time-domain signals to multi-dimensional feature representations. It applies Fourier transforms to convert time signals to frequency spectra, uses wavelet transforms to create time-frequency representations, and extracts multiple features across different dimensions. This dimensional expansion provides richer information for discrimination, enabling more accurate identification of abnormal cardiovascular sounds.
Data Source
AI summary
A method and system for modeling cardiovascular disease using a probability regression model is provided. A parameter estimate of a probability regression model for cardiovascular disease can be generated using predictors derived from cardiovascular sound signals and disease status information. A probability of cardiovascular disease can be generated using a probability regression model that includes a predictor derived from cardiovascular sound signals.