Lung Sound Quality Assessment Using Breathing Pause Segmentation
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Solution Overview
Problem
Accurate diagnosis of heart failure using lung sound analysis is challenging for non-specialists due to the difficulty in determining the quality of lung sounds, particularly when they are of poor quality, leading to potential overlooks in analysis processing.
Innovation Solution
A lung sound analysis system that includes an acquisition means for acquiring time-series acoustic signals, a determination means for identifying breathing pause phases, a dividing means for separating signals into pause and non-pause phases, a calculation means for assessing signal quality based on intensity differences, and a warning means for alerting on poor quality signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If lung sound analysis is performed by non-specialists, then ease of operation is improved, but measurement precision deteriorates due to inability to accurately determine lung sound quality
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between the lung sound acquisition and the diagnosis. The system includes a computer that automatically determines lung sound quality metrics (signal-to-noise ratio, frequency characteristics) and provides assisted diagnosis recommendations, enabling non-specialists to achieve specialist-level measurement precision through the mediating computational analysis
2Productivity
If automated determination of adventitious sounds is performed, then productivity is improved, but reliability deteriorates when poor quality lung sounds are overlooked
Solution Approach 1:
The patent implements a feedback mechanism where the system automatically evaluates lung sound quality metrics (signal-to-noise ratio, spectral characteristics) and provides feedback on the quality assessment. When poor quality sounds are detected, the system flags them for review or requests re-acquisition, ensuring that automated high-productivity analysis does not overlook unreliable data
Solution Approach 2:
The system performs preliminary quality assessment of lung sounds before proceeding to adventitious sound detection. By pre-evaluating signal quality metrics and filtering out poor quality recordings in advance, the system prevents unreliable analysis while maintaining efficient processing of valid data
3Measurement precision
If lung sound quality assessment is performed manually by specialists, then measurement precision is improved, but loss of time increases due to expert requirement
Solution Approach 1:
The patent enables the lung sound analysis system to perform self-assessment of sound quality using automated computational algorithms. The computer automatically calculates signal-to-noise ratios, analyzes frequency spectra, and determines quality metrics without requiring specialist intervention, thereby achieving specialist-level precision while eliminating time loss to expert review
Data Source
AI summary
A lung sound analysis system includes: an acquisition unit that obtains time-series acoustic signals including lung sounds from a heart failure patient; a determination unit that identifies the pause phase of the patient's breathing; a dividing unit that separates the time-series signals into those during the pause phase and those during other phases, based on the determination; a calculation unit that computes an index value representing the quality of the signals outside the pause phase, using the intensity of signals from both phases after separation; and a warning unit that issues alerts based on the calculated index value. The system supports medical decision making for healthcare professionals monitoring heart failure patients.