Lung Sound Analysis System for Heart Failure Detection
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Solution Overview
Problem
Current lung sound analysis systems struggle to detect heart failure exacerbation in patients post-discharge from hospital, as they primarily rely on comparing data from the same patient at different times, lacking a method to analyze lung sounds collected after discharge for early detection of heart failure worsening.
Innovation Solution
A lung sound analysis system that stores time-series acoustic signals from a patient's discharge time as reference signals and acquires and analyzes post-discharge signals to detect abnormalities, using a detection method based on learned normal models from pre-discharge data to identify potential heart failure exacerbation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lung sound analysis systems compare data from the same patient at different times, then they can detect changes in lung conditions, but they cannot effectively detect heart failure exacerbation in patients after discharge from hospital
Solution Approach 1:
The system performs preliminary action by creating a normal model of lung sounds from data collected when the patient is in a normal state (before discharge or during stable period). This pre-established model serves as a reference for future comparisons, enabling the system to detect abnormalities after discharge without requiring real-time medical professional intervention.
Solution Approach 2:
The system creates a copy or model of the patient's normal lung sound characteristics stored in the storage unit. This copied normal state data is then used as a reference template for comparing against post-discharge lung sound data, allowing automated detection of deviations from the normal state without needing the actual patient present during comparison.
2Measurement precision
If skilled medical specialists perform lung sound examination, then accurate diagnosis can be obtained, but it is impossible to obtain detailed diagnosis during routine rounds by general nurses or caring staff
Solution Approach 1:
The system implements self-service by automatically analyzing lung sound data and generating diagnostic information without requiring skilled medical specialists to perform the examination. The automated abnormality detection function allows general nurses or caring staff to obtain detailed diagnosis results by simply collecting and inputting lung sound data, eliminating the need for their direct involvement in complex diagnostic interpretation.
Solution Approach 2:
The system replaces the mechanical system of skilled medical specialists performing manual auscultation and interpretation with an automated electronic analysis system. The electronic stethoscope collects lung sound data, and the processing unit automatically compares it against the stored normal model, substituting human expert analysis with automated computational analysis that maintains high precision while improving accessibility.
3Productivity
If automated systems detect abnormality by comparing patient data with normal and abnormal data obtained in advance, then they can provide diagnostic support, but they cannot utilize post-discharge lung sounds for early detection of heart failure exacerbation
Solution Approach 1:
The system performs preliminary action by establishing the normal model before discharge using lung sound data collected during the patient's hospital stay. This pre-established baseline enables subsequent automated comparisons of post-discharge lung sounds against the patient's own normal state, allowing early detection of heart failure exacerbation that would not be possible with generic normal or abnormal reference data.
Data Source
AI summary
A lung sound analysis system includes a storage means for storing time-series acoustic signals including lung sounds at the time of discharge from hospital of a subject who is a heart failure patient, as reference signals; an acquisition means for acquiring time-series acoustic signals including lung sounds at the determination object time after the discharge from the hospital of the subject, as determination object signals; and a detection means for detecting abnormality in the lung sounds from the determination object signals on the basis of the reference signals.