Fetal Sensor Array Placement for Reliable Heart Rate Signal Detection
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Solution Overview
Problem
Conventional ultrasound-based CTG systems for monitoring maternal and fetal activities require manual adjustment due to fetal position changes, leading to signal loss and poor performance in unsupervised settings, and existing NI-FECG devices lack optimal sensor placement and enhanced signal quality, resulting in unreliable fetal heart rate detection.
Innovation Solution
A system with a monitoring device and computing unit that uses a sensor array, reference electrodes, and a ground electrode to dynamically measure biological data, providing guidance for optimal sensor placement and enhanced signal quality through machine learning models based on maternal and fetal anatomic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ultrasound-based CTG systems are used for monitoring fetal heart rate, then fetal heart rate monitoring is achieved, but signal loss occurs when the ultrasound transducer is not correctly placed due to fetal position changes
Solution Approach 1:
The monitoring device automatically detects fetal heart rate signals and determines optimal sensor placement without requiring manual adjustment by a trained professional. The system self-adjusts to maintain reliable monitoring despite fetal position changes, eliminating the need for continuous human intervention and preventing signal loss.
Solution Approach 2:
The system dynamically adapts to changing fetal positions by continuously assessing signal quality and automatically adjusting sensor placement or selection. This dynamic adaptation ensures continuous reliable monitoring without the signal loss that occurs in static conventional systems when fetal position changes.
2Measurement precision
If conventional ultrasound-based CTG systems require manual adjustment by trained professionals, then accurate fetal heart rate monitoring is achieved, but the system cannot operate continuously in unsupervised settings
Solution Approach 1:
The device performs self-adjustment and self-optimization of sensor placement automatically, eliminating the need for trained professionals to manually adjust the system. This enables continuous unsupervised operation while maintaining measurement precision through automated signal quality assessment and adaptive sensor positioning.
Solution Approach 2:
The system pre-configures multiple sensors in optimal positions and automatically selects the best-performing sensor based on real-time signal quality assessment. This preliminary arrangement of multiple sensors ensures that accurate monitoring can continue without human intervention even when fetal position changes.
3Ease of operation
If existing NI-FECG devices are used without optimal sensor placement guidance, then device operation is simplified, but signal quality is poor and fetal heart rate detection is unreliable
Solution Approach 1:
The system automatically determines optimal sensor placement and configuration without requiring user expertise or manual adjustment. It self-optimizes signal quality by assessing multiple sensor positions and selecting the best configuration, thereby maintaining both ease of operation and detection reliability simultaneously.
Solution Approach 2:
The device continuously monitors signal quality from multiple sensors and uses this feedback to automatically adjust sensor selection and placement. This closed-loop feedback mechanism ensures that the system maintains optimal signal quality and reliable fetal heart rate detection while requiring minimal user input or expertise.
4Reliability
If multiple sensors are deployed to improve signal quality, then fetal signal detection is enhanced, but device complexity increases
Solution Approach 1:
The system divides the monitoring function across multiple sensors arranged in a predefined array, with each sensor contributing to the overall fetal heart rate detection. This segmentation allows the system to improve reliability through redundancy and signal averaging while managing complexity through a structured, modular sensor architecture.
Solution Approach 2:
The system combines signals from multiple sensors to enhance fetal heart rate detection reliability. By merging the output of several sensors and using signal processing techniques, the system achieves improved detection reliability while managing device complexity through integrated signal processing and automated sensor selection algorithms.
Data Source
AI summary
A system for achieving optimal sensor placement and enhanced signal quality for monitoring maternal and fetal activities is disclosed. The system includes a monitoring device and a computing unit. The monitoring device is configured for monitoring maternal and fetal activities and providing guidance to the user via the computing unit upon detecting a feature of interest. The monitoring device includes a plurality of sensors, a data acquisition and transmission unit, one or more reference electrodes, and a ground electrode. Based on personal data acquired using the computing unit, the system utilizes a statistical or machine learning model which incorporates one or more subsets of the personal data to determine the optimal sensor placement close to the fetal heart position. Following sensor placement, the monitoring device performs a signal quality assessment and selects the optimal sensors to ensure reliable information on maternal and fetal activities is obtained.