Fetal Heartbeat Detection Using Div-Conq Signal Segmentation
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Solution Overview
Problem
Current methods for monitoring fetal cardiac activity during pregnancy are inadequate in distinguishing fetal heartbeats from maternal heartbeats and are contaminated with noise signals, making it difficult to accurately assess fetal health.
Innovation Solution
A computer-implemented method using a Div-Conq algorithm that filters phonocardiogram (PCG) signals by applying digital filters, segmenting them into compartments, and classifying these compartments as maternal or fetal heartbeats based on referenced maternal QRS positions, while employing binary classification techniques and Hilbert transform filters to enhance signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current monitoring methods are used to detect fetal cardiac activity, then fetal health monitoring is performed, but the ability to distinguish fetal heartbeats from maternal heartbeats deteriorates and noise contamination increases
Solution Approach 1:
The patent segments the phonocardiogram signal into distinct compartments using the Div-Conq algorithm, separating maternal and fetal heartbeats into different temporal and frequency segments. This segmentation allows independent analysis and classification of each heartbeat type, improving detection accuracy while filtering out noise through systematic signal decomposition
Solution Approach 2:
The patent introduces binary classification techniques and Hilbert transform filters as intermediary processing steps between raw signal acquisition and final heartbeat identification. These intermediaries transform the noisy mixed signal into classified compartments, acting as mediators that separate fetal from maternal heartbeats while eliminating noise contamination
2Measurement precision
If simple monitoring methods are used, then the system remains simple, but the ability to separate fetal heartbeats from maternal heartbeats deteriorates
Solution Approach 1:
The Div-Conq algorithm divides the complex phonocardiogram signal into manageable compartments, making the separation of maternal and fetal heartbeats feasible through systematic segmentation rather than requiring overly complex processing systems
Solution Approach 2:
The patent replaces complex mechanical or invasive monitoring systems with computational signal processing methods, using binary classification and Hilbert transforms to achieve accurate heartbeat separation through software-based analysis rather than hardware complexity
3Reliability
If noise filtering is applied to reduce contamination, then signal quality improves, but the complexity of signal processing increases
Solution Approach 1:
The patent replaces traditional mechanical filtering methods with computational techniques including binary classification and Hilbert transform filters, achieving superior noise reduction and signal quality improvement through algorithmic processing rather than physical filtering mechanisms
Solution Approach 2:
By segmenting the signal into classified compartments, the system applies targeted filtering and processing to specific signal portions, improving overall signal quality while managing processing complexity through localized rather than global processing approaches
Data Source
AI summary
A method includes receiving a plurality of raw PCG data inputs; applying at least one binary classification technique to each of the raw PCG data inputs to generate a respective plurality of filtered PCG data inputs; applying at least one divide-and-conquer algorithm to detect heartbeat compartments in each of the plurality of the filtered PCG data inputs based, at least on part, on assumptions that noise signals and S1-S2 alternation acoustic signals are non-stationary over a one-minute time interval; classifying each compartment of the heartbeat compartments in each of the plurality of the filtered PCG data input as a maternal compartment or a fetal compartment based at least on a plurality of referenced maternal QRS positions; combining a plurality of maternal compartments to identify at least one actual maternal heartbeat; combining a plurality of fetal compartments to identify at least one actual fetal heartbeat.