Representative-Waveform Bio-Signal Quality Assessment Amid Motion Noise
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Solution Overview
Problem
Existing bio-signal quality evaluation methods are compromised by arrhythmia and motion noise, leading to reduced accuracy in estimating blood pressure, particularly in mobile healthcare settings.
Innovation Solution
A method and apparatus that divide bio-signals into sub-signals, extract a representative waveform, and evaluate quality by calculating similarities between these sub-signals, adjusting weights iteratively until a threshold is met, and estimate bio-information based on the quality evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bio-signal quality evaluation is performed using conventional methods, then the evaluation process is simple, but the accuracy of quality assessment is reduced due to arrhythmia and motion noise
Solution Approach 1:
The patent divides the bio-signal into multiple sub-signals corresponding to different heartbeat cycles. By segmenting the signal, the system can evaluate each sub-signal individually and identify high-quality segments for blood pressure estimation, thereby improving overall assessment accuracy despite the increased processing complexity
Solution Approach 2:
The patent implements an iterative weight adjustment mechanism where weights assigned to different sub-signals are dynamically modified based on quality evaluation results. The system repeatedly evaluates quality and adjusts weights until convergence, allowing the evaluation process to adapt and improve accuracy through dynamic optimization
2Measurement precision
If the bio-signal is divided into multiple sub-signals and iterative weight adjustment is performed, then the quality assessment accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing computational resources only on evaluating and weighting sub-signals that contain relevant blood pressure information, rather than processing the entire bio-signal uniformly. This selective approach improves accuracy while reducing overall computational burden
Solution Approach 2:
The iterative weight adjustment process uses feedback from quality evaluation results to refine weights in subsequent iterations. The system monitors convergence and stops when weights stabilize, preventing excessive computation while ensuring accurate quality assessment
Data Source
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AI summary
A method of evaluating quality of a bio-signal includes receiving an input bio-signal; setting a quality evaluation region in thebio-signal; dividing a signal of the quality evaluation region into a plurality of sub-signals; extracting a representative waveform by using the plurality of sub-signals; evaluating a quality of each sub-signal based on the representative waveform; and evaluating a quality of the signal in the quality evaluation region based on quality evaluation results of the plurality of sub-signals.