RF Reflectometric Cardiac Sensing with Phase Auto-Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing remote sensing methods for cardiac data are sensitive to factors like relative position, movement, and interfering signals, leading to inconsistent reproducibility and inefficiency in medical diagnostics.
Innovation Solution
A system and method involving reflectometric detection using radio frequency signals, including high pass filtering, waveform phase position determination, auto-correlation, and heart rate computation to consistently measure cardiac activity without physical contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote sensing methods using radio frequency signals are used to measure cardiac data, then physical contact is eliminated and subject comfort is improved, but measurement precision and reproducibility deteriorate due to sensitivity to relative position, movement, and interfering signals
Solution Approach 1:
The system continuously monitors the reflected radio frequency signal characteristics and adjusts signal processing parameters in real-time to compensate for position variations and movement artifacts, maintaining measurement precision without physical contact
Solution Approach 2:
The system dynamically changes signal processing parameters such as filtering thresholds, integration times, and frequency bands based on detected signal quality and subject movement patterns, optimizing measurement reproducibility under varying remote sensing conditions
2Measurement precision
If complex signal processing steps (filtering, phase determination, auto-correlation) are implemented to improve measurement precision, then reproducibility of cardiac data is improved, but device complexity increases
Solution Approach 1:
The signal processing is divided into distinct modular stages: high-pass filtering to remove low-frequency artifacts, phase position determination to extract cardiac timing, and auto-correlation to identify periodic patterns. Each module handles a specific aspect of signal refinement, making the overall complex process more manageable and implementable
Solution Approach 2:
High-pass filtering is applied as a preliminary step before phase determination and auto-correlation to pre-remove low-frequency noise and movement artifacts. This preliminary action simplifies subsequent processing steps by reducing the complexity of the input signal that needs to be analyzed
3Measurement precision
If high pass filtering with cutoff frequency of 10 Hz or greater is applied to remove noise, then measurement precision is improved, but loss of information occurs in the filtered signal
Solution Approach 1:
The system applies high-pass filtering with a cutoff frequency of 10 Hz or greater to remove low-frequency noise and movement artifacts, accepting that some information in the filtered signal may be lost. This partial action is sufficient to achieve the desired measurement precision for cardiac data without requiring complete preservation of all signal components
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
The solution enables reliable and efficient remote sensing of cardiac data, improving reproducibility and reducing the complexity of medical diagnostics by filtering out noise and variations in signal processing.
Implementation Method 1
modulation of the phase and/or frequency of a reflected radio frequency signal (i.e., radar or Doppler radar techniques) to provide a measurement of pulse rate and/or respiration rate
Implementation Method 2
modulation of the phase and/or frequency of a reflected radio frequency signal (i.e., radar or Doppler radar techniques)
Data Source
AI summary
A method for remotely sensing cardiac-related data of an animal subject includes transmitting a RF signal to impinge on tissue of the subject, receiving a reflected portion of the RF signal, generating baseband data, filtering baseband data (e.g., including high pass filtering), performing waveform phase position determination, performing at least one auto-correlation of the waveform phase position determined data, determining periodicity of the auto-correlated data, and (i) computing heart rate using a maximum periodicity of the periodicity, or (ii) identifying abnormalities in cardiac function, such as may be indicated by temporal variations in heart rate and/or signal amplitude corresponding to cardiac activity. Multiple bandpass filtering schemes may be employed.