Wireless Respiratory Sensor Using Neural Network Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing respiratory disorders like obstructive sleep apnea are cumbersome, costly, and require specialized equipment and trained staff, making them unsuitable for home use and easy patient installation.
Innovation Solution
A wireless sensor system that records sound and motion signals using a microphone and motion sensor, converting them into digital data streams for pre-filtering and analysis through a multi-layer neural network to identify respiratory events, with a monitoring station that processes data to classify respiratory episodes and generate a confidence factor for disorder identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography is used to diagnose respiratory disorders, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent extracts the essential diagnostic function from complex polysomnography by using only two simple sensors (microphone and motion sensor) to capture respiratory events. The neural network processes these minimal inputs to achieve accurate diagnosis without requiring the full polysomnography apparatus
Solution Approach 2:
The patent replaces complex mechanical and electronic polysomnography equipment with a simple wireless sensor system that uses acoustic and motion signals processed by a neural network algorithm, eliminating the need for cumbersome laboratory equipment
2Measurement precision
If polysomnography is used to diagnose respiratory disorders, then measurement precision is improved, but ease of operation worsens due to requiring trained staff
Solution Approach 1:
The system performs self-diagnosis through automated neural network processing of sensor data, eliminating the need for trained medical staff to interpret results. The device automatically identifies respiratory events and generates diagnostic information
Solution Approach 2:
The patent replaces the need for trained personnel with an automated neural network system that processes sensor data and identifies respiratory events, making the diagnostic process accessible without specialized medical training
3Ease of operation
If simple wireless sensors are used for respiratory monitoring, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms simple sensor inputs into diagnostic information by changing the processing parameters through neural network analysis. The raw acoustic and motion signals are converted into meaningful respiratory event classifications through sophisticated algorithmic processing
Solution Approach 2:
The system combines multiple simple sensor types (acoustic and motion sensors) to create a composite measurement system that achieves diagnostic accuracy comparable to complex equipment by leveraging the complementary information from different sensing modalities
4Ease of operation
If home diagnosis systems are made simple for patient use, then ease of operation improves, but reliability deteriorates due to installation difficulties
Solution Approach 1:
The patent extracts the diagnostic function into a simple wireless sensor that can be easily placed on the patient without complex installation procedures, while maintaining reliable diagnosis through automated neural network processing that compensates for the simplicity of the sensing approach
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 accurate and user-friendly diagnosis of respiratory disorders at home by converting sound and motion signals into digital data streams for analysis, providing a reliable and efficient method for identifying respiratory events with a high confidence factor, reducing the need for complex equipment and trained staff.
Implementation Method 1
sound and motion signals, which are generated during respiration, are recorded by means of a wireless sensor equipped with a microphone sensor and a motion sensor
Implementation Method 2
sound and motion signals, which are generated during respiration, are recorded by means of a wireless sensor equipped with a microphone sensor and a motion sensor
Data Source
AI summary
The present disclosure relates to a method and a system for examining respiratory disorders whereby signals coming from the examined person are recorded by a wireless sensor equipped with a microphone and an accelerometer and then sent to a monitoring station. The monitoring station receives a digital data stream from the wireless sensor, cuts out respiratory episodes from the signal and, using a classification assembly constructed from three independent detection modules, classifies a respiratory episode as being normal or as snoring as well as determines the occurrence of apnoea.