QRS Onset Detection via Adaptive Temporal Windowing
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Solution Overview
Problem
Current methods for detecting QRS onset and offset points in electrocardiogram signals are inadequate, despite various proposed schemes, as they fail to accurately and efficiently locate these points for effective monitoring of cardiovascular conditions.
Innovation Solution
A heart monitor system that uses an analog-to-digital converter and processor to generate an ST deviation time series through a recursive exponential average filter, locating the ST point by finding the maximum slope of the QRS and searching within adaptive windows to determine the ST and PQ points, thereby calculating the ST deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fixed-window methods are used for QRS detection, then the detection process is simple, but the accuracy of locating QRS onset and offset points deteriorates
Solution Approach 1:
The patent applies dynamics by making the search window adaptive rather than fixed. The window size and position are dynamically adjusted based on the instantaneous frequency and morphology of the ECG signal. This allows the detection algorithm to adapt to varying heart rates and signal characteristics, improving the accuracy of QRS onset and offset point detection while managing complexity through algorithmic adaptability.
Solution Approach 2:
The patent changes key parameters of the detection process based on signal characteristics. The search window parameters (size, position, duration) are modified according to the detected signal frequency and morphology. This parameter adaptation enables accurate detection across different cardiac conditions and heart rates without requiring a completely complex redesign of the detection framework.
2Measurement precision
If adaptive windowing is implemented to improve detection accuracy, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The patent segments the ECG signal into distinct phases (QRS complex, ST segment, T wave) and applies specific detection strategies to each segment. The adaptive windowing is concentrated on the QRS detection phase, while subsequent ST point detection uses the identified QRS boundaries as reference. This segmentation reduces overall computational burden by focusing adaptive processing only where most needed.
Solution Approach 2:
The patent performs preliminary QRS complex detection and boundary identification before proceeding to ST point detection. By first establishing the QRS onset and offset points using adaptive windowing, the system creates a foundation that simplifies subsequent ST segment analysis. This preliminary action reduces the computational load of the overall process by breaking it into manageable stages.
Data Source
AI summary
A heart monitor computes ST segment deviation as the difference in the value of an electrocardiogram signal at the ST and PQ points of a heartbeat. The ST point is found based on slope criteria and temporal criteria. The maximum (positive) slope of the QRS (maxQRS) is located. Within a preset window after the maxQRS point, the processor searches for a sample at which the second finite difference is less than a threshold that is a function of average QRS amplitude. If such a qualifying sample is found, the processor examines its location relative to the location of an adaptive window that is centered on a sample that is an adaptively determined distance from the peak of the R wave. If the qualifying point is within the adaptive window, it is chosen as the ST point. If the qualifying point is after the adaptive window, the ST point is set at the end of the adaptive window. Finally, if the qualifying point is before the adaptive window, the ST point is selected at the beginning of the adaptive window. The adaptive window is then updated according to an exponential average filter based upon the distance between the ST point and the R wave peak.