Auscultatory Sound Analysis System for Telemedicine
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Solution Overview
Problem
Current auscultatory sound analysis systems rely on subjective judgments by medical professionals, who need specialized training to diagnose diseases based on visualized sound frequencies, amplitudes, and time information, which is challenging in real-time clinical environments, especially for conditions like COVID-19 where timely intervention is critical.
Innovation Solution
An auscultatory sound analysis system that converts in-body sounds into digital data, generates a spectrogram, and outputs signal component strengths along a time axis, enabling objective and visual analysis of specific frequencies or frequency ranges, facilitating remote monitoring and telemedicine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If auscultatory sounds are visualized with frequency, time, and amplitude information, then objective diagnostic information is provided, but specialized training is required for examiners to interpret the visual information
Solution Approach 1:
The system extracts and highlights only the relevant frequency components associated with specific diseases from the complex spectrogram data. By isolating and emphasizing disease-specific frequency ranges, the system provides objective diagnostic information while reducing the interpretation burden on examiners, as they only need to recognize patterns in predetermined frequency ranges rather than analyze the entire frequency spectrum.
Solution Approach 2:
The system changes the parameter representation by converting raw spectrogram data into disease-specific indicator values based on signal strength in predetermined frequency ranges. This transformation converts complex multi-dimensional audio data into simplified, clinically-relevant parameters that are easier for examiners to interpret while maintaining diagnostic objectivity.
2Loss of information
If full spectrogram information is displayed, then complete sound analysis is available, but it is difficult to grasp chronological changes in signal component strengths
Solution Approach 1:
The system extracts only the signal strength information at predetermined frequency ranges associated with specific diseases from the complete spectrogram. By removing irrelevant frequency components and temporal details, the system presents only the chronologically-relevant diagnostic information, making it easy to track disease progression over time without losing the essential diagnostic content.
Solution Approach 2:
The system transforms the three-dimensional spectrogram data (frequency, time, amplitude) into a one-dimensional time-series representation showing signal strength changes at specific frequencies. This dimensional reduction converts complex spectral information into an easily interpretable chronological trend that clearly shows disease progression while preserving the essential diagnostic information.
3Loss of time
If real-time auscultatory sound analysis is performed, then timely diagnosis is achieved, but subjective judgment by examiners is still required
Solution Approach 1:
The system performs automated analysis by automatically calculating signal strengths in predetermined frequency ranges and generating diagnostic indicators without requiring examiner interpretation. The system serves itself by converting raw audio data into objective diagnostic information through automated signal processing, eliminating subjective judgment while maintaining real-time diagnostic capability.
Solution Approach 2:
The system replaces the mechanical process of subjective examiner judgment with automated computational analysis. By using algorithms to objectively measure signal strengths and generate diagnostic indicators, the system substitutes human subjectivity with precise, repeatable computational measurements, achieving both timely diagnosis and objective precision.
Data Source
AI summary
An auscultatory sound analysis system that makes it possible to chronologically understand qualitative changes in an auscultatory sound from an auscultation device that contributes to telemedicine avoiding medical practice involving users' close contact, in medical care having a possibility of a contact infection such as COVID-19. The auscultatory sound analysis system converts the auscultatory sound into digital data, further performing a spectrogram conversion, and chronologically outputting, along a time axis, strengths of a signal component at a specific frequency or in a specific frequency range. The system thereby makes it possible to output, to transfer data of, and to display quantitative and temporal changes in high-pitch crepitations caused by interstitial pneumonia from COVID-19 and to thus understand a development status of the disease.