Adaptive Weighting for ECG Motion Artifact Reduction
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Solution Overview
Problem
Existing methods for reducing motion artifacts in ECG signals, such as those described in US 6216031B1 and US 5908393A, still suffer from unacceptable notches and distortions, and do not adequately improve the signal-to-noise ratio (SNR) despite using low-pass and high-pass filtering techniques.
Innovation Solution
An adaptive weight determination method is employed, based on the correlation between the current and previous ECG beats, to dynamically assign weights for calculating the mean value beat, allowing for piecewise filtering of residual signals to enhance noise reduction and maintain important ECG features, thereby improving the SNR and removing motion artifacts effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If normal LP/HP filtering is used to remove noises, then muscle noise and baseline wander are reduced, but unacceptable notches and distortions appear in the ECG signal
Solution Approach 1:
The patent applies different filtering strategies to different segments of the ECG signal. By identifying characteristic ECG waves (P wave, QRS complex, T wave) and applying segment-specific filtering, the method removes noise while preserving the unique morphology of each wave type, thus avoiding the notches and distortions caused by uniform filtering across the entire signal.
Solution Approach 2:
The filtering approach is made adaptive and dynamic by adjusting filter parameters based on the detected ECG morphology. The system dynamically selects filtering strength and type according to the local characteristics of the signal, allowing effective noise removal in some segments while preserving important features in others, thereby avoiding fixed-filter limitations.
2Device complexity
If fixed weights are assigned to previous mean value beat and current beat, then the calculation is simple, but the method cannot adapt to abrupt ECG morphology changes
Solution Approach 1:
The patent implements feedback by using the detected ECG morphology characteristics to inform subsequent filtering and weight assignment decisions. The system continuously monitors the signal, identifies morphological changes, and adjusts its processing parameters accordingly, creating a closed-loop system that adapts to changing conditions rather than applying fixed processing rules.
Solution Approach 2:
The weight assignment mechanism transitions from static to dynamic by adjusting weights based on real-time ECG morphology detection. When morphological changes are detected, the system dynamically modifies the weighting between previous mean value beat and current beat, allowing adaptive response to abrupt changes while maintaining computational efficiency.
Data Source
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AI summary
The present invention provides a method and apparatus for reducing motion artifacts in ECG signals. According to an aspect of the present invention, there is proposed a method of reducing motion artifacts in ECG signals, comprising: acquiring a current beat from a continuously measured ECG signal of a patient; calculating a correlation coefficient between a previous mean value beat and the current beat in the ECG signal; determining the weights to be assigned to the previous mean value beat and the current beat based on the correlation coefficient; and calculating a current mean value beat based on the previous mean value beat, the current beat, and the weights thereof. Accordingly, the novel method of deriving the current mean value beat may reduce ECG artifacts due to patient movement in such a manner that the SNR of the ECG signal can be improved substantially.