J-Point Detection in ECG Signals Using R-Peak Distance
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Solution Overview
Problem
Conventional algorithms for locating the J-point in an electrocardiogram (ECG) waveform are prone to errors due to electronic noise, artifacts, and physiological changes, and have long computation times, making them inefficient for accurately detecting myocardial ischemia and infarction.
Innovation Solution
The J-point is determined by identifying the sample within the window between the S-peak and the T-peak that has the smallest distance to the R-peak, using a method that scales the ECG data and calculates distances from the R-peak to the midpoint between the S-peak and T-peak, allowing for precise location of the J-point and measurement of ST segment deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional algorithms are used to locate the J-point in ECG waveforms, then the detection can be performed with standard methods, but the computation time becomes long and accuracy is reduced due to noise and artifacts
Solution Approach 1:
The algorithm divides the ECG waveform into distinct segments (P-wave, QRS complex, T-wave) and identifies key features (peaks, valleys, inflection points) within each segment. By segmenting the analysis into specific time windows around these features, the algorithm efficiently locates the J-point without requiring exhaustive computation across the entire waveform, thus reducing computation time while maintaining accuracy.
Solution Approach 2:
The algorithm performs preliminary identification of the R-peak and T-peak locations before searching for the J-point. By establishing these reference points first and using them to define the search window for the J-point, the algorithm narrows down the computational scope significantly, enabling faster and more accurate localization without examining the entire ECG signal.
2Reliability
If conventional algorithms are used to locate the J-point, then standard processing can be applied, but the results are prone to errors from electronic noise, artifacts, and physiological changes
Solution Approach 1:
The algorithm introduces intermediate reference points (R-peak, T-peak, S-peak) that serve as mediators to locate the J-point. Rather than directly searching for the J-point in noisy conditions, the algorithm uses these more easily identifiable peaks as anchor points to define a constrained search region, thereby improving reliability by reducing sensitivity to noise and artifacts while maintaining manageable algorithmic complexity.
Solution Approach 2:
The algorithm dynamically adjusts search parameters (time windows, amplitude thresholds, slope criteria) based on the detected peak locations and local waveform characteristics. By adapting these parameters to the specific morphology of each ECG beat, the algorithm improves its ability to distinguish the true J-point from noise and artifacts, enhancing detection reliability without requiring overly complex fixed-rule algorithms.
Data Source
AI summary
The current subject matter determines the location of the J-point in an ECG signal by examining ECG samples within a window of samples between the S-peak and the T-peak. The sample in this range with the smallest distance, Δd, to the R-peak is selected as the J-point. Thus, the J-point location can be determined based on an ECG sample's distance to the R-peak. The J-point location can then be used to determine whether there is elevation or depression of the ST segment. Related apparatus, systems, techniques, and articles are also described.