ECG Q and J Point Detection Using Waveform Length Transform
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Solution Overview
Problem
Conventional methods for measuring the ST-segment level in electrocardiograms face challenges in reliably locating the Iso-electric and J points, particularly in real-time and ambulatory monitoring, due to high computational load, sensitivity to QRS morphology changes, and incorrect noise estimation, leading to false alarms and missed events.
Innovation Solution
A system and method that determine the Q and J points of an electrocardiogram using a processor to receive beat-cycle waveforms from multiple leads, assess signal quality, and combine waveforms with good quality using a Waveform Length Transform (WLT) to identify these points, ensuring robust lead selection and noise tolerance for accurate ST-segment level measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual determination of fixed time intervals u and z is used for each patient, then the approach introduces extra work load to clinicians, but the fixed intervals do not adapt to changes of QRS morphology causing measurement errors
Solution Approach 1:
The patent implements dynamic adaptation of time intervals u and z based on detected QRS morphology characteristics. The system automatically adjusts these intervals for each beat rather than using fixed manual values, allowing the measurement parameters to adapt to changing cardiac conditions while reducing clinician workload.
Solution Approach 2:
The system performs automatic detection and adjustment of time intervals without requiring manual intervention. The algorithm autonomously identifies QRS morphology changes and recalibrates the measurement parameters, enabling the system to self-adjust and eliminating the need for continuous manual configuration by clinicians.
2Extent of automation
If automatic detection of Iso-electric point and J point is performed from averaged beat-cycle ECG waveform, then the computational load increases, but the approach provides automated searching capability
Solution Approach 1:
The patent divides the ECG signal analysis into distinct segments: QRS complex detection, P wave detection, and ST segment analysis. By segmenting the waveform and focusing computational resources on specific regions of interest, the system achieves automatic detection while reducing overall computational load compared to analyzing the entire beat cycle.
Solution Approach 2:
The system performs preliminary detection of QRS complexes and identifies candidate regions for Iso-electric and J point detection before conducting the full automatic search. This preliminary action narrows down the search space and reduces the computational effort required for the subsequent automatic detection phase.
3Reliability
If noise detection is performed using Karhunen-Loeve transform feature vectors for ST segment and QRS complex, then the amount of calculation is relatively high, but the approach enables noise detection capability
Solution Approach 1:
The patent extracts only the essential feature vectors from the ECG signal that are most indicative of noise - specifically focusing on ST segment and QRS complex characteristics. By extracting and analyzing only these critical features rather than processing the entire signal, the system achieves reliable noise detection with reduced computational requirements.
4Object-affected harmful factors
If beat averaging is performed to reduce ECG noise level, then the ECG noise level is reduced, but the approach requires information of neighboring beats which may not be available in real-time monitoring
Solution Approach 1:
The patent introduces an intermediary approach where instead of requiring full beat averaging over a time window, the system uses real-time noise estimation based on current signal characteristics and applies adaptive filtering. This intermediary method reduces noise effectively while operating with minimal time delay, making it suitable for real-time monitoring where neighboring beats may not be available.
Data Source
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AI summary
A system for determining the Q and J points of an electrocardiogram (ECG)combines a WLT-based Q, J detection algorithm with signal quality assessment for lead selection. A Q, J detector (24) receives a beat-cycle waveform for the beat under consideration from each of a plurality (N) of ECG leads, and assesses signal quality for each lead using signal quality assessor (SQA) components 261, 262,... 26N. The leads with "good" signal qualities are employed for a multichannel waveform length transform (WLT), which yields a combined waveform length signal (CWLS). The Q and J points are then determined from the CWLS.