Multi-Sensor Confidence for Accurate Respiratory Rate Measurements
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Solution Overview
Problem
Respiratory rate measurements derived from a single sensor or sensor type can be less accurate due to noise, sensor limitations, and body movement, necessitating improved methods for enhancing the accuracy and confidence in these measurements.
Innovation Solution
A patient monitoring system that combines multiple physiological signals from different sensors, such as acoustic and optical sensors, to derive and refine respiratory rate measurements, using comparative metrics to determine multiparameter confidence and output a combined respiratory rate measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors and physiological signals are integrated to improve measurement accuracy, then reliability and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent combines multiple physiological signals from different sensor types (acoustic respiratory signals, photoplethysmographic signals, ECG signals, bioimpedance signals) into a unified respiratory rate measurement system. The processor integrates these diverse signals to derive respiratory rate measurements and calculate confidence levels, resolving the contradiction by merging multiple data sources to improve reliability while managing complexity through systematic signal integration
Solution Approach 2:
The monitoring system is designed to process multiple types of physiological signals simultaneously using a single integrated processor that can derive respiratory rate from various signal sources. This multi-functional approach allows the system to use different sensors (acoustic, optical, electrical) for the same measurement goal, improving reliability through redundancy while avoiding the need for separate dedicated systems for each signal type
2Measurement precision
If multiple physiological signals are processed to enhance measurement accuracy, then measurement precision improves, but processing requirements and computational load increase
Solution Approach 1:
The patent segments the complex processing task into distinct functional modules: acoustic signal processing for respiratory rate derivation, photoplethysmographic signal analysis, ECG signal processing, and a confidence calculation module. Each module handles a specific aspect of signal processing independently, then the processor integrates these segmented results to achieve high measurement precision while managing computational complexity through modular design
Solution Approach 2:
The system uses the processed physiological signals to generate confidence levels that feedback into the overall measurement assessment. The processor continuously evaluates the quality and consistency of multiple signal sources, using this feedback to adjust and refine the respiratory rate measurement, thereby improving measurement precision through iterative validation while maintaining manageable processing requirements
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
Enhances the accuracy and confidence in respiratory rate measurements by integrating data from multiple sensors, providing a more reliable output for clinical decision-making.
Implementation Method 1
an optical sensor comprising: a light emitter configured to impinge light on body tissue of a living patient, the body tissue comprising pulsating blood, and a detector responsive to the light after attenuation by the body tissue, wherein the detector is configured to generate a photoplethysmographic signal
Implementation Method 2
an acoustic sensor, the acoustic sensor configured to obtain an acoustic respiratory signal from the living patient
Data Source
AI summary
This disclosure describes, among other features, systems and methods for using multiple physiological parameter inputs to determine multiparameter confidence in respiratory rate measurements. For example, a patient monitoring system can programmatically determine multiparameter confidence in respiratory rate measurements obtained from an acoustic sensor based at least partly on inputs obtained from other non-acoustic sensors or monitors. The patient monitoring system can output a multiparameter confidence indication reflective of the programmatically-determined multiparameter confidence. The multiparameter confidence indication can assist a clinician in determining whether or how to treat a patient based on the patient's respiratory rate.