Doppler Fetal Heart Rate Detection With Supervisory Error Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Doppler-based fetal heart rate (FHR) monitoring systems face challenges with autocorrelation algorithms being sensitive to noise and variability, leading to inaccurate interpretations due to factors like artifacts, maternal and fetal movement, and computational complexity, especially at the extreme ends of the FHR range.
Innovation Solution
A fetal monitoring system employing a parallel supervisory signal processing technique with windowing-based peak detection and SNR calculation to automatically detect and correct errors like half-count and double-count errors, and adaptively adjust signal processing to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autocorrelation algorithms are used for FHR detection, then the system can detect fetal heart rate, but the system becomes sensitive to noise and signal quality issues leading to inaccurate results
Solution Approach 1:
The patent introduces a supervisory error detection component as an intermediary that monitors and validates the output of the autocorrelation algorithm. This component detects errors such as half-count and double-count mistakes by analyzing the autocorrelation results and comparing them against expected physiological patterns, thereby improving reliability without eliminating the use of autocorrelation for FHR detection
Solution Approach 2:
The system implements feedback mechanisms where the supervisory component continuously monitors the autocorrelation algorithm's output and provides corrective feedback when errors are detected. This feedback loop allows the system to identify and correct inaccurate FHR measurements caused by noise or signal quality issues, improving both precision and reliability
2Productivity
If autocorrelation algorithms are used for FHR detection, then the system can process doppler signals, but computational complexity increases requiring significant processing power and time
Solution Approach 1:
The patent segments the signal processing task by dividing it into two distinct components: the primary autocorrelation-based FHR detection algorithm and the supervisory error detection algorithm. This segmentation allows each component to be optimized independently, with the supervisory component focusing only on error detection rather than full signal processing, thereby reducing overall computational complexity while maintaining processing speed
3Adaptability or versatility
If standard autocorrelation algorithms are used, then the system can detect FHR, but it cannot adapt to variations in signal characteristics leading to false interpretations
Solution Approach 1:
The patent introduces dynamic adaptability through the supervisory error detection component, which can adjust its monitoring parameters and error detection thresholds based on the characteristics of the incoming doppler signal. This dynamic behavior allows the system to adapt to variations in signal quality, maternal movement, and fetal position while maintaining accurate FHR interpretation through real-time adjustments
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 system provides real-time error detection and correction, reducing false alarms and improving clinical outcomes by enhancing the accuracy of FHR monitoring, especially at extreme ranges, and optimizing performance through adaptive averaging and repositioning recommendations.
Implementation Method 1
The Doppler effect is a change in frequency or wavelength of a wave (in this case, ultrasound waves) when the source of the wave and the observer are in relative motion. In the context of FHR monitoring, the Doppler effect is used to detect and measure the fetal heartbeat and fetal movement.
Data Source
AI summary
Techniques for real-time doppler ultrasound signal processing techniques for fetal heart rate (FHR) detection with supervisory error control are provided. In an example, a method comprises receiving, by a system comprising a processor, signal data as the signal data is captured via an ultrasound transducer during a fetal monitoring session using doppler-based ultrasound technology. The method further comprises determining, by the system using a first process, first FHR values for a fetal heart targeted by the ultrasound transducer based on the signal data as the signal data is received, and determining using a second process in parallel with the first process, second FHR values for the fetal heart based on the signal data as the signal data is received. The method further comprises determining, by the system, whether the first FHR values are associated with an error based on differences between the first FHR values and the second FHR values.