Computer-Aided Lung Disease Diagnosis Using Digital Signal Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing lung diseases through chest auscultation rely heavily on human interpretation, which is subjective and ineffective for detecting sounds outside the human hearing range, leading to incomplete diagnosis and reliance on skilled practitioners.
Innovation Solution
A digital signal processing system using a stethoscope, microphone, and computer-based analyzer that applies autoregressive modeling and LPC/PARCOR coefficients to classify lung sounds, enabling the identification of lung diseases without human interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human practitioners perform chest auscultation, then diagnosis can be performed with simple equipment, but the human ear cannot detect sounds outside its frequency range (15-20,000 Hz) leading to incomplete diagnosis
Solution Approach 1:
The patent replaces the human ear and subjective interpretation with electronic transducers, amplifiers, and digital signal processing systems. The transducer converts lung sounds to electrical signals, the amplifier boosts frequencies beyond human hearing range, and the computer analyzes spectral characteristics to objectively diagnose lung conditions, thereby extending detection capability from 15-20,000 Hz to a broader frequency spectrum.
Solution Approach 2:
The patent introduces intermediate processing devices between the lung sounds and the diagnostician. These include transducers that convert acoustic signals to electrical signals, amplifiers that enhance specific frequency ranges, and computer-based spectral analysis systems that process the signals. These intermediaries enable detection and analysis of sounds outside the human hearing range while providing objective diagnostic data.
2Reliability
If clinicians use chest auscultation in a cursory manner, then the examination process is quick and simple, but the lack of skilled recognition leads to inaccurate diagnosis
Solution Approach 1:
The patent enables the lung sounds themselves to provide diagnostic information without requiring extensive human interpretation. The computer-based system automatically analyzes spectral characteristics, identifies patterns associated with specific lung conditions, and generates diagnostic recommendations. This self-service approach allows accurate diagnosis even when performed by non-experts, eliminating the need for lengthy training while maintaining high reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the computer analysis system provides objective diagnostic feedback based on spectral characteristics of lung sounds. The system compares detected sound patterns against known disease signatures and provides immediate diagnostic feedback, allowing clinicians to quickly assess lung conditions with high accuracy without requiring extensive subjective interpretation skills.
3Measurement precision
If extensive training is provided for chest auscultation, then practitioners can recognize lung sounds better, but the training is long and difficult and still limited by human hearing range
Solution Approach 1:
The patent replaces the need for extensive human training with automated electronic detection and analysis systems. The computer-based spectral analysis automatically identifies lung sound characteristics and disease patterns without requiring practitioners to undergo long training periods. The system handles the complexity of pattern recognition, allowing non-experts to achieve expert-level diagnostic accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system mechanizes and standardizes lung disease diagnosis, improving accuracy and reliability by processing a wide frequency range of lung sounds, including those inaudible to humans, and effectively differentiates between various lung conditions.
Implementation Method 1
a transducer operatively connected to the stethoscope for converting the lung sounds to an electric signal
Data Source
AI summary
A method and apparatus by which lung deceases are identified uses computer analysis of sound signals that are picked up from various locations on the chest walls of a subject by a modified stethoscope. The modification includes a small microphone in one of the hoses of the stethoscope. Signals from the microphone are input to a computer such as a personal computer or PC for processing. The computer extracts from these signals features which are dominant for particular lung diseases. A classifier classifies these features, determines if the lungs are diseased, and identifies the disease.