ECG Signal Quality Assessment Using Kurtosis and Wavelet Transform
Solution Overview
Problem
Existing ECG signal monitoring technologies face challenges in distinguishing and mitigating the impact of motion artifacts and baseline wander, which can overwhelm the ECG signal, leading to unreliable heart rate and physiological data measurements.
Innovation Solution
A wireless sensor device with a processor and memory system that calculates the Kurtosis of the ECG signal, compares it to a threshold, and uses low pass IIR filters to assess signal quality, ignoring unreliable portions and providing alerts for noise sources like motion artifacts and improper contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ECG signal monitoring is performed continuously, then heart rate and physiological data can be measured, but motion artifacts and baseline wander can overwhelm the ECG signal and make measurements unreliable
Solution Approach 1:
The patent segments the ECG signal analysis by dividing it into multiple frequency components using wavelet transform. This allows separation of the ECG signal from motion artifacts and baseline wander, which occupy different frequency bands. The signal is processed in discrete wavelet coefficients, enabling targeted filtering of harmful components while preserving the useful ECG information.
Solution Approach 2:
The patent introduces wavelet transform as an intermediary mathematical tool that mediates between the raw ECG signal and the final processed output. This intermediary transformation allows for multi-resolution analysis, where different frequency components can be selectively enhanced or suppressed, effectively separating the ECG signal from harmful artifacts without direct manipulation of the original signal.
2Measurement precision
If traditional noise filtering methods are used, then some noise can be reduced, but they cannot effectively distinguish between different noise sources and the ECG signal
Solution Approach 1:
The patent employs dynamic thresholding in the wavelet domain, where noise thresholds are adaptively determined based on the statistical properties of the signal at each decomposition level. This dynamic approach allows the filtering mechanism to adjust to varying signal characteristics and noise conditions, improving measurement precision without requiring overly complex fixed-structure filters.
Solution Approach 2:
The patent changes the representation parameters of the ECG signal by transforming it into the wavelet domain with multiple decomposition levels. This parameter transformation allows selective manipulation of different frequency components, enabling precise noise reduction while maintaining the underlying ECG signal structure. The wavelet coefficients serve as new parameters that simplify the noise filtering process.
Data Source
AI summary
A method and system for assessing an electrocardiogram (ECG) signal quality are disclosed. In a first aspect, the method comprises determining a Kurtosis calculation of the ECG signal and determining whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to determine a Kurtosis calculation of the ECG signal and to determine whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality.