Respiratory Phase Detection via Acoustic Pause Intervals
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Solution Overview
Problem
Current techniques for recognizing respiratory phases from tracheal sound signals lack accuracy, especially when signals are below a certain threshold or contaminated with noise, making it difficult to distinguish inhalation and exhalation phases, particularly in complex respiratory profiles like snoring or apnea.
Innovation Solution
A method that detects pause intervals in acoustic signals to identify inhalation and exhalation phases by comparing the duration and position of these pauses, using frequency and energy processing to enhance recognition, and employing machine learning for indeterminate intervals, allowing for more precise phase determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional frequency or time domain analysis is used to detect respiratory phases, then the method is simple to implement, but the accuracy deteriorates when signals are below a certain threshold or contaminated with noise
Solution Approach 1:
The patent transforms the acoustic signal from the time domain to the frequency domain using Fast Fourier Transform (FFT), and further to the time-frequency domain using Short-Time Fourier Transform (STFT). This dimensional transformation allows identification of respiratory phases based on frequency spectral characteristics rather than direct amplitude analysis, enabling accurate detection even when signal amplitude is low or noisy.
Solution Approach 2:
The patent changes the analysis parameters from time-domain amplitude thresholds to frequency-domain spectral characteristics. By computing power spectral density and identifying frequency peaks corresponding to respiratory sounds, the system can detect respiratory phases based on frequency patterns rather than amplitude thresholds, improving accuracy in low-signal conditions.
2Reliability
If multiple measuring instruments are used to measure respiratory behavior, then the reliability of diagnosis is improved, but the device complexity and invasiveness increase
Solution Approach 1:
The patent extracts and analyzes only the acoustic signal component from the respiratory system using a single tracheal sound sensor. By focusing specifically on the acoustic characteristics of respiratory sounds and applying sophisticated signal processing, the system achieves reliable respiratory phase detection without requiring multiple invasive sensors or complex monitoring equipment.
Solution Approach 2:
The patent replaces mechanical/respiratory monitoring instruments (such as flow sensors, pressure transducers, or chest straps) with an acoustic measurement system. By substituting mechanical respiratory monitoring with acoustic signal analysis, the system achieves similar diagnostic reliability through a simpler, less invasive single-sensor approach.
3Quantity of substance
If the acoustic signal threshold is lowered to capture more respiratory data, then the quantity of information is improved, but the measurement precision deteriorates due to increased noise interference
Solution Approach 1:
The patent uses frequency-domain analysis to separate respiratory signals from noise. By transforming the signal to the frequency domain and analyzing spectral characteristics, the system can identify respiratory phases based on frequency patterns even when the time-domain amplitude is low, effectively increasing the quantity of detectable information without sacrificing precision.
Solution Approach 2:
The patent introduces frequency spectral analysis as an intermediary between the raw acoustic signal and the respiratory phase identification. This intermediary transformation allows the system to extract meaningful respiratory information from low-amplitude or noisy signals by analyzing the frequency spectrum rather than direct amplitude thresholds.
Data Source
AI summary
A method for determining respiratory phases in an acoustic signal which represents an individual's respiratory activity. The respiratory activity includes inhalation and exhalation phases. The method includes: detecting pause intervals in the acoustic signal; and determining time intervals corresponding to inhalation phases each between a long pause and a short pause, and time intervals corresponding to exhalation phases each between a short pause and a long pause.