ECG Signal De-noising via Wavelet Transform Segmentation
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Solution Overview
Problem
Existing ECG monitoring systems struggle with noise reduction and accurate analysis, particularly with hand-held devices using dry electrodes, which can introduce artifacts and lead to inaccurate diagnosis.
Innovation Solution
The described apparatuses and methods involve extracting and de-noising ECG signals by identifying putative regions, correlating them to determine highly correlated regions, and differentially processing these regions relative to the rest of the signal, using techniques such as filtering and cross-correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hand-held ECG devices with dry electrodes are used, then ease of operation is improved, but signal quality deteriorates due to motion artifacts and contact artifacts
Solution Approach 1:
The patent segments the ECG signal processing into multiple stages: initial filtering to remove obvious artifacts, followed by wavelet transform decomposition into different frequency components, selective processing of each component, and reconstruction. This segmentation allows targeted removal of motion artifacts while preserving genuine cardiac signals.
Solution Approach 2:
The patent changes the parameter representation of the ECG signal by transforming it from the time domain to the wavelet domain. This parameter transformation enables better separation of signal components, allowing the system to identify and remove artifacts based on their distinct wavelet coefficient patterns while maintaining the underlying cardiac rhythm information.
2Measurement precision
If signal filtering and cleaning are applied before analysis, then measurement precision is improved, but loss of information occurs when non-noise signal components are removed
Solution Approach 1:
The patent applies local quality by treating different frequency components of the ECG signal differently through wavelet decomposition. Each wavelet coefficient represents a local feature of the signal, and the algorithm selectively modifies only those coefficients that correspond to artifacts (such as high-frequency motion artifacts) while leaving coefficients representing genuine cardiac features (such as QRS complexes and T waves) unchanged. This localized processing in the wavelet domain achieves noise removal without information loss.
3Reliability
If extensive filtering is applied to remove artifacts, then reliability of diagnosis is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex multi-stage analog filtering hardware with a computational approach using wavelet transform algorithms. Instead of using multiple physical filters with different characteristics, the system uses software-based wavelet decomposition and coefficient thresholding, which can be implemented on mobile devices and processors. This substitution reduces hardware complexity while maintaining or improving artifact removal effectiveness.
Data Source
AI summary
Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.