Adaptive Lung Sound Analysis for Heart Failure Exacerbation Detection
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Solution Overview
Problem
Existing lung sound analysis systems fail to provide detailed and accurate diagnoses of heart failure outside specialized medical settings, leading to a high risk of overlooking heart failure exacerbation due to incomplete auscultation.
Innovation Solution
A lung sound analysis system that stores auscultation history, calculates abnormal sound frequencies at specific positions, determines the sequence of auscultation based on these frequencies, and guides operators to acquire time-series acoustic signals from prioritized positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed sequence of auscultation positions is used, then the auscultation process is simple and standardized, but heart failure exacerbation may be overlooked when auscultation is terminated early at any position
Solution Approach 1:
The patent applies dynamics by making the auscultation sequence adaptive rather than fixed. The system dynamically adjusts the sequence of auscultation positions based on real-time detection results at each position. When abnormal sounds are detected, the system prioritizes checking other positions to ensure comprehensive coverage, preventing early termination from overlooking exacerbation while maintaining operational simplicity through automated guidance.
2Reliability
If auscultation is performed at all positions regardless of abnormal sound detection, then comprehensive examination is achieved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by using historical data and machine learning to predict which auscultation positions are most likely to reveal abnormal sounds. The system performs preliminary analysis of patient history and risk factors to prioritize positions before actual auscultation begins, enabling comprehensive examination while reducing time by focusing first on high-probability positions.
Solution Approach 2:
The system implements feedback by continuously monitoring detection results at each auscultation position and adjusting the remaining sequence based on these results. When abnormal sounds are detected at a position, the system feeds this information back to reorder subsequent positions, ensuring comprehensive coverage of potentially affected areas while avoiding redundant checks in already-confirmed normal regions, thus optimizing time efficiency.
3Adaptability or versatility
If general nurses or caring staff perform auscultation, then diagnosis can be obtained outside specialized settings, but detailed and accurate diagnosis is difficult to achieve
Solution Approach 1:
The patent applies the intermediary principle by introducing an automated analysis system that mediates between the operator (nurse or caring staff) and the diagnosis. The system provides real-time guidance on which positions to check and interprets the findings, enabling non-specialists to perform accurate diagnoses outside specialized settings while maintaining measurement precision through computational analysis support.
Solution Approach 2:
The system replaces the mechanical system of relying solely on operator skill with an automated electronic analysis system. Machine learning algorithms and pattern recognition replace the need for highly trained ears, enabling general nurses and caring staff to achieve diagnosis accuracy comparable to specialists while maintaining adaptability in various settings.
Data Source
AI summary
A lung sound analysis system includes a storage means for storing therein the history of auscultation observations on lung sound data of each of auscultation positions of a subject who is a heart failure patient, a calculation means for calculating appearance frequency of abnormal sounds for each of the auscultation positions on the basis of the history, a determination means for determining the sequence of the auscultation positions at which lung sounds are to be auscultated from the subject, on the basis of the calculated appearance frequency, and an acquisition means for giving guidance on the auscultation positions of the subject to an operator according to the determined sequence, and acquiring time-series acoustic signals including lung sounds from a guided auscultation position. The system supports medical decision-making for healthcare professionals monitoring heart failure patients.