Respiratory Rate Processing via Wavelet Band Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing respiratory rate processing systems face challenges in accurately estimating respiratory rates due to noise artifacts, signal spikes, and transient amplitude changes, which can lead to inaccuracies and loss of breath rate processing, especially during conditions like apnea or lead detachment.
Innovation Solution
The system employs a sensor to detect impedance across a patient's chest cavity, followed by signal pre-conditioning using filters to reduce noise, adaptive clipping based on local amplitude statistics, and quarter-phase domain periodicity tracking with a wavelet bank to estimate respiratory rates, ensuring accurate breath rate estimation even during transient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional respiratory rate processing is used, then the system is simpler, but accuracy deteriorates due to noise artifacts and signal spikes
Solution Approach 1:
The patent divides the respiratory signal processing into multiple frequency bands using a wavelet transform bank, separating the signal into different octave bands. This segmentation allows targeted analysis of specific frequency ranges (0.1-0.5Hz, 0.2-0.5Hz, 0.4-0.5Hz) to identify respiratory rate while filtering out noise and artifacts in other frequency ranges.
Solution Approach 2:
The patent introduces an intermediary amplitude thresholding step between wavelet decomposition and spectral analysis. By applying adaptive amplitude thresholds to each wavelet band, the system filters out signal spikes and transient artifacts before they contaminate the respiratory rate calculation, improving measurement accuracy without requiring complex noise rejection algorithms.
2Measurement precision
If noise filtering is applied, then measurement accuracy improves, but loss of information increases due to potential removal of valid signal components
Solution Approach 1:
The patent applies different processing characteristics to different frequency bands and signal regions. Each wavelet octave band has its own amplitude thresholding parameters and spectral analysis methods tailored to that frequency range. This local quality approach ensures that noise filtering is applied selectively where needed while preserving valid respiratory signal components in other frequency regions.
Solution Approach 2:
The patent uses dynamic amplitude thresholding that adapts to the local signal characteristics in each wavelet band. Rather than applying a fixed global threshold, the system calculates amplitude thresholds dynamically for each frequency band based on the observed signal statistics, allowing the filtering to adapt to changing signal conditions and preserve valid breath rate information while removing noise.
3Reliability
If adaptive clipping is used, then reliability improves during transient conditions, but device complexity increases
Solution Approach 1:
The patent applies preliminary amplitude thresholding to each wavelet band before performing spectral analysis and respiratory rate calculation. By pre-filtering each frequency band with adaptive amplitude thresholds, the system removes signal spikes and transient artifacts early in the processing chain, preventing them from corrupting the subsequent respiratory rate estimation and improving reliability during adverse conditions.
Solution Approach 2:
The patent implements feedback through adaptive amplitude thresholding that continuously monitors signal characteristics in each wavelet band and adjusts thresholds accordingly. The system uses the observed signal amplitude distribution to dynamically set thresholds that distinguish valid respiratory signals from noise and artifacts, creating a feedback loop that maintains processing integrity during transient conditions like apnea or lead detachment.
Data Source
AI summary
In one aspect, a computer-implemented method includes receiving a signal corresponding to impedance across a patient's chest cavity; filtering the signal using one or more filters that reduce noise and center the signal around a zero baseline; adjusting an amplitude of the filtered signal based on a threshold value; separating the amplitude-adjusted signal into component signals, where each of the component signals represents a frequency-limited band; detecting a fractional phase transition of a component signal of the component signals; selecting a dominant component signal from the component signals based on amplitudes of the component signals at a time corresponding to the detected fractional phase transition; determining a frequency of the dominant component signal at the time corresponding to the detected fractional phase transition; and determining a respiratory rate of the patient based on the determined frequency.