Digital Stethoscope Lung Sound Analysis for Bovine Respiratory Diagnosis
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Solution Overview
Problem
Current methods for diagnosing bovine respiratory diseases are inefficient due to the lack of automated systems for analyzing auscultated sounds from cattle, which are complex to treat and diagnose compared to human respiratory diseases, and existing technologies have not effectively utilized digital stethoscopes for bovine species.
Innovation Solution
A system and method using a digital stethoscope to collect and analyze bovine lung sounds, converting them into numerical lung scores through mathematical operations and algorithms, which are then compared to threshold values to diagnose and treat respiratory diseases, with additional filtering techniques to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual evaluation and rectal temperature measurement are used for diagnosing bovine respiratory diseases, then the diagnostic process is simple to perform, but the measurement precision and reliability of diagnosis are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/visual assessment methods with an electronic digital stethoscope system that captures, processes, and analyzes lung sounds digitally. This substitution enables objective, quantifiable measurement of respiratory conditions through acoustic signal processing, thereby improving measurement precision while managing system complexity through automated analysis algorithms.
Solution Approach 2:
The patent introduces an intermediary computational system that processes the raw audio data from the stethoscope through multiple algorithms (Fourier transform, wavelet transform, neural networks) to generate lung scores. This intermediary layer translates complex acoustic signals into interpretable diagnostic metrics, improving diagnosis accuracy without requiring direct expert interpretation of raw sounds.
2Reliability
If automated auscultation analysis systems are implemented for bovine respiratory disease diagnosis, then measurement precision and diagnostic objectivity improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the diagnostic system into distinct functional modules: audio capture (digital stethoscope), signal preprocessing (filtering, segmentation), feature extraction (Fourier transform, wavelet transform), classification (neural networks, decision trees), and output generation (lung scores, diagnosis recommendations). This modular segmentation improves reliability through specialized processing at each stage while managing overall system complexity through clear separation of functions.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors lung sound patterns, compares them against established criteria and historical data, and adjusts diagnostic decisions accordingly. The feedback loop validates diagnostic conclusions through multiple algorithmic approaches and provides confidence metrics, thereby improving diagnosis reliability while using computational feedback to simplify the interpretation process.
3Measurement precision
If multiple filtering techniques and mathematical operations are applied to auscultated sounds, then measurement precision and disease detection accuracy improve, but processing time and computational requirements increase
Solution Approach 1:
The patent applies preliminary filtering and preprocessing operations to the raw audio signals before main analysis, including noise filtering, signal segmentation into respiratory cycles, and frequency domain transformation. These preliminary actions prepare the data in advance, reducing the computational burden of subsequent complex operations and enabling faster, more accurate lung score calculation without sacrificing measurement precision.
Solution Approach 2:
The patent employs multiple mathematical operations and filtering techniques that may exceed the minimum required for basic diagnosis. By applying comprehensive processing including Fourier transform, wavelet transform, and multiple classification algorithms, the system ensures high measurement precision and robustness. The computational overhead is managed through efficient implementation and selective application based on diagnostic needs.
Data Source
AI summary
A system and method are provided for diagnosis of animal respiratory diseases using auscultation techniques. Animal lung sounds are recorded and stored as digitized data. Algorithms are applied to the data producing an output indicative of the health of the animal. Another method determines effectiveness of antibiotics administered to animals based upon observed relationships between lung score categories obtained from auscultation. A comparison is made between sample populations of animals that receive different classifications of antibiotics, and this data is compared to lung score categories observed for each of the animals that received the antibiotics. A statistical analysis is conducted to confirm statistical differences in case fatality rates between lung score categories. Insignificant differences in mortality rates across a range of lung score categories indicates the particular antibiotic administered is not effective, whereas a reduced fatality rate associated with lower lung scores indicates a level of antibiotic effectiveness.