Digital Stethoscope Convolution for Real-Time Abnormality Detection
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Solution Overview
Problem
Existing digital stethoscopes struggle with real-time detection and forecasting of respiratory abnormalities, inability to predict severity or characteristics of respiratory events, and lack of privacy-protected data cataloging for in-home use.
Innovation Solution
A digital stethoscope system utilizing convolutional methods for real-time detection, cough counting, respiratory event prediction, and future event forecasting, integrated with a base station for enhanced processing and wireless charging, while ensuring privacy through cloud-based data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional stethoscopes are used for respiratory diagnosis, then a doctor can listen to patient's breathing, but the system requires a doctor to be present and is prone to error
Solution Approach 1:
The patent replaces the mechanical acoustic detection system (traditional stethoscope requiring manual listening) with an electronic digital stethoscope that uses microphones, audio filters, and classification algorithms to automatically detect and analyze respiratory sounds, eliminating the need for a doctor to be physically present while improving detection reliability through computational methods
2Reliability
If digital stethoscopes use classification algorithms to distinguish normal from abnormal noises, then detection capability is improved, but real-time performance is insufficient for local execution
Solution Approach 1:
The patent extracts and removes unnecessary computational complexity from the classification algorithm, retaining only the essential features and parameters needed for accurate respiratory abnormality detection. This streamlined algorithm can be executed in real-time on local devices without requiring complex processing infrastructure, enabling immediate analysis of respiratory sounds
3Reliability
If current classification systems predict whether a noise is normal or abnormal, then basic detection is achieved, but the ability to forecast future respiratory events and predict severity is lacking
Solution Approach 1:
The patent implements preliminary analysis of respiratory sound patterns and features to predict future respiratory events before they occur. By continuously monitoring and analyzing audio signals against trained models, the system can forecast potential exacerbations and alert users in advance, transforming passive detection into proactive prediction and enabling early intervention
4Productivity
If data from in-home stethoscopes is collected for analysis, then monitoring capability is improved, but patient privacy interests are compromised
Solution Approach 1:
The patent extracts and analyzes only the essential audio features and patterns from respiratory sounds, discarding unnecessary personal identifying information and raw audio data. The system performs analysis locally on the device and transmits only aggregated, anonymized statistics to remote servers, ensuring that patient privacy is protected while maintaining effective monitoring capability
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
Enables real-time detection of respiratory abnormalities, counts coughs, predicts event severity, and forecasts future respiratory events, all while protecting patient privacy and facilitating remote data storage.
Implementation Method 1
receiving, from a microphone, an auditory signal
Implementation Method 2
A convolution procedure is performed on the auditory spectrogram to generate one or more convolution values
Data Source
AI summary
Embodiments disclosed herein improve digital stethoscopes and their application and operation. A first method detects of a respiratory abnormality using a convolution. A second method counts coughs for a patient. A third method predicts a respiratory event based on a detected trend. A fourth method forecasts characteristics of a future respiratory event. In a fifth embodiment, a base station is provided for a digital stethoscope.