Auscultatory Sound Sensor Array for CAD Detection
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Solution Overview
Problem
Current coronary artery disease detection systems face challenges in accurately sensing auscultatory sound signals due to issues with sensor attachment and noise interference, which can lead to incorrect diagnoses.
Innovation Solution
The system employs multiple auscultatory sound sensors placed strategically on the thorax, coupled with a data recording module and docking system that preprocesses and analyzes breath-held auscultatory sound signals to detect decoupling and noise, using a scale factor determination process and noise filtering techniques to ensure accurate data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple auscultatory sound sensors are placed on the thorax to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the detection task into multiple segments by placing multiple sensors at specific locations on the thorax (anterior, lateral, posterior aspects). Each sensor captures sound signals from a specific region, and the data processing module integrates these segmented signals to achieve comprehensive detection of coronary artery disease indicators.
Solution Approach 2:
The data processing module performs multiple functions: it processes signals from multiple sensors simultaneously, detects various types of auscultatory sounds (murmurs, gallops, rubs), determines sensor decoupling status, and provides diagnostic recommendations. This multi-functional approach consolidates complexity into a single processing unit rather than requiring separate systems for each function.
2Measurement precision
If scale factor determination and noise filtering techniques are applied to minimize noise interference, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by determining scale factors and applying noise filtering techniques before final diagnostic analysis. The data processing module pre-processes the raw sensor signals by adjusting scale factors based on initial analysis and filtering out noise components, ensuring that the subsequent diagnostic algorithms work with cleaned, standardized data.
Solution Approach 2:
The system implements feedback mechanisms where the data processing module continuously monitors signal quality, detects noise levels, and adjusts processing parameters accordingly. The module provides feedback on sensor decoupling detection and signal validity, allowing real-time optimization of the detection process without requiring manual intervention.
3Reliability
If sensor decoupling detection is implemented to ensure reliable sensor attachment, then reliability is improved, but device complexity increases
Solution Approach 1:
The sensor system performs self-service by automatically detecting its own attachment status. The data processing module monitors the signals from each sensor to determine if a sensor has become decoupled from the patient's body, and can identify which specific sensor requires reattachment. This self-diagnostic capability eliminates the need for separate monitoring systems or manual checking procedures.
Data Source
AI summary
A Support Vector Machine trained responsive to mean and median values of standard and Shannon energy for a plurality of time and frequency intervals within a heart cycles provides for detecting coronary artery disease (CAD). A quality of an auscultatory sound time-series vector is assessed responsive to a vector distance and angle thereof in relation to a median heart cycle vector.