ECG Signal Processing for MRI R-Wave Detection
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Solution Overview
Problem
Existing ECG signal processing systems face challenges in detecting R-waves with low delay and high accuracy, especially in noisy environments like MRI machines, where abnormal waveforms are present, due to interference from dynamic noise caused by pulsed gradient magnetic fields and RF magnetic fields.
Innovation Solution
A signal processing apparatus that includes a storage circuit and processing circuitry to generate and store detection parameters for R-wave detection, enhance ECG signals using filters, generate templates for R-wave detection, and update these templates to improve detection accuracy and robustness, even in noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG signal processing is used, then the system is simple, but detection accuracy deteriorates in noisy environments like MRI
Solution Approach 1:
The patent segments the ECG signal processing into multiple stages: preprocessing to remove power line noise, QRS detection to identify candidate waves, and R-wave identification using amplitude and timing criteria. This segmentation allows targeted noise reduction at each stage, improving overall detection accuracy in MRI environments.
Solution Approach 2:
The system uses feedback mechanisms where detected QRS complexes and R-waves inform subsequent detection parameters. The timing and amplitude characteristics of detected waves are fed back to adjust detection thresholds and improve accuracy in distinguishing true R-waves from noise artifacts.
2Measurement precision
If complex noise filtering is applied, then detection accuracy improves, but processing delay increases
Solution Approach 1:
The patent applies preliminary noise filtering and preprocessing steps before R-wave detection to reduce the computational burden during critical detection phases. Power line noise removal and baseline correction are performed in advance, allowing faster real-time R-wave identification with minimal delay.
Solution Approach 2:
The system applies selective filtering only where most needed - aggressive noise reduction for power line interference and baseline drift, but minimal filtering for the actual R-wave detection to preserve timing accuracy. This partial action approach balances noise reduction with delay minimization.
3Productivity
If simple detection algorithms are used, then processing speed is fast, but reliability deteriorates for abnormal waveforms
Solution Approach 1:
The patent applies different detection criteria to different parts of the ECG signal. For normal sinus rhythms, simple amplitude and timing thresholds are used for fast detection. For abnormal waveforms, the system adjusts local detection parameters based on waveform characteristics, applying more rigorous criteria only where needed to maintain reliability without sacrificing overall processing speed.
Solution Approach 2:
The detection algorithm dynamically adapts to changing ECG conditions. Detection thresholds and parameters are adjusted in real-time based on the detected waveform morphology and rhythm regularity. This dynamic adaptation maintains high reliability for both normal and abnormal waveforms while preserving processing speed through intelligent parameter adjustment rather than fixed complex algorithms.
Data Source
AI summary
In one embodiment, a signal processing apparatus includes a storage circuit and processing circuitry configured to (a) generate detection parameters for detecting a specific signal included in a biosignal relevant to a heartbeat, based on a waveform of the biosignal, (b) store the detection parameters in the storage circuit, (c) detect the specific signal by using the detection parameters, and (d) generate a synchronization signal for performing heartbeat synchronization imaging based on the specific signal.