Digital Stethoscope Respiratory Abnormality Detection
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Solution Overview
Problem
Current digital stethoscopes face limitations in real-time performance for detecting respiratory abnormalities, inability to forecast future respiratory events, and inadequate data cataloging while ensuring patient privacy, with existing systems requiring a doctor's presence and lacking severity prediction capabilities.
Innovation Solution
A digital stethoscope system that uses convolutional neural networks to analyze auditory signals, tracks coughs, predicts respiratory event severity, and forecasts future events by generating classification values and transmitting data to a cloud-based service, incorporating a base station for enhanced processing and wireless charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional stethoscopes are used for respiratory diagnosis, then a doctor can manually detect abnormalities, but the system requires constant doctor presence and cannot provide continuous monitoring
Solution Approach 1:
The patent replaces the mechanical manual listening system with a digital system that uses microphones, processors, and machine learning algorithms to automatically detect and classify respiratory sounds, enabling continuous automated monitoring without requiring a doctor's constant presence
Solution Approach 2:
The digital stethoscope system performs self-diagnosis by automatically analyzing respiratory sounds using embedded processors and machine learning models, classifying abnormalities without requiring external medical intervention for each measurement
2Measurement precision
If classification algorithms are used to detect abnormal noises, then detection accuracy improves, but real-time performance is insufficient for immediate intervention
Solution Approach 1:
The system performs preliminary processing by pre-processing audio signals through filtering and feature extraction before classification, preparing the data in advance to enable faster real-time decision-making while maintaining high detection precision
Solution Approach 2:
The detection system is segmented into distinct functional modules including audio capture, signal processing, feature extraction, and classification, allowing each component to be optimized independently for both precision and real-time performance
3Quantity of substance
If continuous monitoring is implemented, then more respiratory data can be collected, but patient privacy and data security become more vulnerable
Solution Approach 1:
The system extracts and processes only the essential features needed for diagnosis locally on the device, while transmitting only anonymized results to the cloud, separating the sensitive raw data from the transmission channel to minimize privacy exposure
Solution Approach 2:
The patent introduces local processing as an intermediary layer between the microphone and cloud server, where data is processed and anonymized locally before being transmitted, acting as a protective barrier that reduces privacy risks during transmission and storage
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.