Respiration Estimation Using Multi-Source Signal Selection
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Solution Overview
Problem
Existing respiration estimation methods face challenges in accuracy due to individual differences, artifacts from body motion, and variations in signal quality, particularly when using R-wave amplitude, R-R interval, and acceleration data, which restricts the measurement of short respiratory cycles and affects the Signal-to-Noise ratio.
Innovation Solution
A method involving R-wave amplitude detection, R-R interval detection, and triaxial acceleration displacement analysis, followed by Fourier transformation to extract and select the best frequency data for respiration estimation, reducing the influence of artifacts and individual differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If R-wave amplitude, R-R interval, and acceleration data are used for respiration estimation, then multiple signal sources are available for analysis, but measurement accuracy deteriorates due to individual differences, body motion artifacts, and signal quality variations
Solution Approach 1:
The patent applies partial action by selectively using only the necessary components from each signal source (R-wave amplitude, R-R interval, and acceleration data) rather than processing all components. The respiratory frequency is extracted only when signal quality thresholds are met, avoiding unnecessary processing that would introduce errors from individual differences and body motion artifacts.
Solution Approach 2:
The patent changes parameters by introducing signal quality assessment metrics and threshold values for R-wave amplitude and acceleration data. When signal quality falls below thresholds (indicating poor contact impedance or excessive body motion), the corresponding data sources are excluded from respiration estimation, thereby maintaining measurement precision despite using multiple signal sources.
2Measurement precision
If conventional respiration measurement methods (mask, thermistor, chest band, electrical impedance meter) are used, then respiration can be measured directly, but the subject experiences unnatural feelings and reduced comfort
Solution Approach 1:
The patent replaces mechanical respiration measurement systems (masks, thermistors, chest bands) with an electrical/cardiac-based system using ECG electrodes and acceleration sensors. This substitution eliminates the need for physical contact with respiratory pathways while capturing respiration information through cardiac signal modulation and body acceleration, thereby improving wearing comfort without sacrificing measurement precision.
Solution Approach 2:
The patent uses cardiac signals and acceleration data as intermediary indicators to infer respiration frequency indirectly. Instead of directly measuring respiratory flow or volume, the system captures respiration-related modulations in ECG signals and body acceleration, providing a comfortable indirect measurement approach that maintains measurement accuracy.
3Adaptability or versatility
If R-R interval is used for respiration estimation, then cardiac potential data can be utilized, but estimation results vary significantly due to autonomic nervous system influence, age, and mental condition
Solution Approach 1:
The patent applies feedback by continuously assessing signal quality metrics for R-R interval data and comparing them against threshold values. When autonomic nervous system influence or other factors cause R-R interval variability to exceed thresholds, the system adjusts by excluding this data source or reducing its weight in the final respiration estimation, thereby maintaining consistency despite physiological variations.
Solution Approach 2:
The patent makes the respiration estimation system dynamic by adaptively selecting and weighting different signal sources (R-wave amplitude, R-R interval, acceleration data) based on real-time signal quality assessment. This dynamic approach allows the system to optimize measurement precision by using only the most reliable data sources under current physiological conditions, rather than rigidly relying on R-R interval alone.
4Adaptability or versatility
If R-wave amplitude is used for respiration estimation, then cardiac potential data can be utilized, but measurement error occurs due to contact impedance changes from body motion and skin condition variations
Solution Approach 1:
The patent applies preliminary action by assessing signal quality metrics (including contact impedance indicators) before using R-wave amplitude data for respiration estimation. By pre-evaluating the reliability of R-wave amplitude signals and comparing them against threshold values, the system prevents the introduction of measurement errors from body motion and skin condition variations, ensuring only high-quality data is utilized.
Solution Approach 2:
The patent changes parameters by introducing signal quality thresholds and assessment metrics for R-wave amplitude data. When contact impedance changes cause R-wave amplitude variability to exceed acceptable thresholds, the system adjusts by excluding this data source from respiration estimation, thereby maintaining measurement precision despite the availability of cardiac potential data.
5Adaptability or versatility
If acceleration data is combined with cardiac potential data using weighted mean, then respiration estimation can be performed, but respiratory sampling is restricted by cardiac cycle and body motion artifacts affect signal quality
Solution Approach 1:
The patent applies partial action by selectively integrating only the necessary components from acceleration data and cardiac potential data based on signal quality assessment. Rather than always using weighted mean of all three sources (R-wave amplitude, R-R interval, acceleration), the system uses only those sources that meet quality thresholds, avoiding the introduction of body motion artifacts and cardiac cycle restrictions into the final estimation.
Solution Approach 2:
The patent changes parameters by introducing dynamic weighting factors and threshold values for multi-source data integration. Instead of fixed weighted mean, the system adaptively adjusts the weight or inclusion of each data source (R-wave amplitude, R-R interval, acceleration) based on real-time signal quality assessment, thereby maintaining measurement precision while preserving the benefits of multi-source integration.
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
This approach improves respiration estimation accuracy by selecting the best frequency data from multiple sources, enhancing the robustness against body motion and individual variations, and maintaining accuracy even with degraded Signal-to-Noise ratios.
Implementation Method 1
a Fourier transform unit configured to Fourier-transform each of a time-series signal of the R-wave amplitude, a time-series signal of the R-R interval, and a time-series signal of the angular displacement to obtain a frequency spectrum of each of the signals of the R-wave amplitude, R-R interval, and the angular displacement
Data Source
AI summary
There is provided a respiration estimation apparatus. The respiration estimation apparatus includes an R-wave amplitude detection unit (5) configured to detect an amplitude of an R wave from a cardiac potential waveform of a subject, an R-R interval detection unit (6) configured to detect an R-R interval as an interval between an R wave and an immediately preceding R wave from the cardiac potential waveform, an acceleration displacement detection unit (7) configured to detect an angular displacement of an acceleration vector from a triaxial acceleration signal by a respiratory motion of the subject, a Fourier transform unit (10) configured to Fourier-transform each of time-series signals of the R-wave amplitude, the R-R interval, and the angular displacement to obtain a frequency spectrum of each of the signals of the R-wave amplitude, the R-R interval, and the angular displacement, and a signal selection unit (11) configured to extract a frequency as a candidate of a respiration frequency of the subject from each of the frequency spectrum of the R-wave amplitude, the frequency spectrum of the R-R interval, and the frequency spectrum of the angular displacement, and select best data from the frequencies as the respiration frequency of the subject.