Auscultatory Sensor Debond Detection via Signal Scaling
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Solution Overview
Problem
Current coronary artery disease detection systems face challenges in accurately sensing auscultatory sound signals from the thorax, as existing sensors may become detached or debonded from the skin, leading to unreliable data acquisition and poor signal quality, especially for cardiovascular conditions characterized by low sound levels.
Innovation Solution
The auscultatory coronary-artery-disease detection system employs multiple auscultatory sound sensors strategically placed on the thorax, coupled with a data recording module and docking system that includes a debond-detection process to ensure sensors remain attached, using a scale factor and debond-detection threshold to validate signal quality and exclude detached or debonded sensors from data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If auscultatory sound sensors are placed on the thorax to detect cardiovascular sounds, then the detection capability of abnormal cardiovascular conditions is improved, but the sensors may become detached or debonded from the skin leading to unreliable data acquisition
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor attachment status through signal quality assessment before data collection becomes unreliable. The debond-detection process actively checks for sensor detachment in real-time, allowing early intervention or data exclusion before attachment failure compromises the entire measurement session.
Solution Approach 2:
The system implements feedback mechanisms by continuously evaluating signal quality metrics and comparing them against predefined thresholds. When signal quality deteriorates indicating potential sensor debonding, the system provides feedback through the debond-detection process to adjust data acquisition parameters or exclude compromised data, ensuring maintained measurement reliability.
2Reliability
If multiple auscultatory sound sensors are strategically placed on the thorax to improve detection accuracy, then the reliability of data acquisition is improved, but the complexity of the system increases
Solution Approach 1:
The system applies universality by implementing a unified debond-detection process that handles multiple sensors through a single standardized methodology. The same signal quality assessment algorithms and attachment status evaluation procedures are applied across all auscultatory sound sensors, reducing the complexity that would arise from sensor-specific monitoring approaches.
Solution Approach 2:
The system merges the monitoring function with the existing data acquisition process. Rather than adding separate independent monitoring systems for each sensor, the debond-detection process is integrated into the signal processing pipeline, combining attachment status verification with routine signal quality assessment to minimize additional system complexity.
3Measurement precision
If signal quality validation using scale factor and debond-detection threshold is implemented, then the accuracy of auscultatory sound signal acquisition is improved, but the processing time and computational requirements increase
Solution Approach 1:
The system applies partial action by implementing a tiered validation approach where basic signal quality checks are performed on all data continuously, while more comprehensive debond-detection analysis is applied selectively based on initial assessment results. This staged processing reduces overall computational burden while maintaining high accuracy for critical detection.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting processing intensity based on signal characteristics. When signals exhibit stable quality metrics within acceptable ranges, processing operates at baseline efficiency. When parameters indicate potential debonding or quality degradation, the system intensifies analysis only for affected data segments, optimizing the balance between accuracy and processing time.
Data Source
AI summary
At least one of at least one data-dependent scale factor or at least one data-dependent detection threshold is determined responsive to at least one block of time-series data of an auscultatory-sound signal generated by an auscultatory-sound sensor operatively coupled to a portion of the skin of a test-subject, wherein the at least one data-dependent scale factor or at least one data-dependent detection threshold provides a measure of a range of values of the at least one block of time-series data in relation to a predetermined metric, and is used to determine whether or not the auscultatory-sound sensor is either debonded or detached from the skin of the test-subject.