ECG Signal Processing Apparatus for MRI Synchronization
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Solution Overview
Problem
Existing ECG signal processing systems face challenges in accurately detecting the R-wave in ECG signals, especially in noisy environments like MRI machines, which can lead to erroneous detection and delayed synchronization in medical imaging.
Innovation Solution
An ECG-signal processing apparatus that utilizes multiple biological signals from different leads to calculate peak time differences, creating a database of these differences to improve R-wave detection accuracy by distinguishing between R-wave and noise-based peaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise filtering is applied to ECG signals during MRI imaging, then signal quality improves, but detection delay increases
Solution Approach 1:
The system performs preliminary detection of potential R-waves by monitoring ECG signal peaks before formal verification. This allows the system to prepare for upcoming R-waves in advance, reducing the effective detection delay while maintaining accuracy through subsequent verification steps.
Solution Approach 2:
The patent introduces an intermediary verification mechanism that uses multiple ECG leads and peak time difference analysis to confirm R-wave detection. This intermediary step filters out noise-induced false positives without requiring extensive signal processing that would cause delays.
2Device complexity
If single-lead ECG detection is used, then device complexity is reduced, but detection accuracy deteriorates due to noise
Solution Approach 1:
The detection system is segmented into multiple independent lead channels, each processing ECG signals separately. By dividing the detection task across multiple leads rather than using a single complex processing path, the system achieves improved accuracy through comparison while keeping each individual processing path relatively simple.
Solution Approach 2:
The patent combines information from multiple ECG leads by analyzing peak time differences between them. This merging of multiple simple detection results produces a more accurate overall detection outcome, allowing the system to achieve high precision without requiring each individual lead to be highly complex.
3Measurement precision
If aggressive noise removal is applied to ECG signals, then signal clarity improves, but false detection rate increases
Solution Approach 1:
The system implements feedback through cross-validation of peak detections across multiple ECG leads. When a peak is detected in one lead, the system checks corresponding peaks in other leads and verifies that peak time differences match expected physiological patterns. This feedback mechanism confirms true R-waves while rejecting noise-induced false positives.
Solution Approach 2:
The detection thresholds and verification criteria are dynamically adjusted based on the observed signal characteristics and noise levels. Rather than using fixed aggressive filtering, the system adapts its detection parameters to maintain signal clarity while minimizing false detections caused by varying noise conditions during MRI imaging.
Data Source
AI summary
In one embodiment, a signal processing apparatus that is configured to be connected to an imaging apparatus includes: a memory configured to store a predetermined program; and processing circuitry configured, by executing the predetermined program, to detect respective peaks of a plurality of biological signals related to heartbeat of plural leads, calculate difference in peak time between the plurality of biological signals, and detect a specific waveform included in the plurality of biological signals based on the difference in peak time.