Computer analysis of the electrocardiogram during esophageal pacing cardiac stress
H Jadvar1, J M Jenkins, R E Stewart
1Pritzker Institute of Medical Engineering, Illinois Institute of Technology, Chicago 60616.
Insights
A new algorithm accurately analyzes electrocardiograms (ECGs) during transesophageal atrial pacing stress tests. This method overcomes challenges posed by pacing artifacts, improving cardiac stress assessment for patients unable to exercise.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Many patients with coronary artery disease cannot complete exercise stress tests.
- Transesophageal atrial pacing offers a noninvasive alternative for cardiac stress induction.
- Standard ECG analysis software struggles with pacing artifacts during transesophageal atrial pacing.
Purpose of the Study:
- To develop a robust signal processing algorithm for ECG interpretation during transesophageal atrial pacing.
- To address limitations of current computer methods in analyzing ECGs with pacing artifacts.
- To improve the accuracy of cardiac stress assessment in non-ambulatory patients.
Main Methods:
- Developed a novel signal processing algorithm using linear and nonlinear transformations.
- Implemented schemes to differentiate pacing artifacts from QRS complexes.
- Incorporated logic for automatic recognition of sustained pacing capture.
- Calculated beat-by-beat and averaged ST segment parameters.
- Measured heart rate, RR interval, pace-to-R interval, R-wave amplitude, and sinus node recovery time.
Main Results:
- The algorithm successfully detects and differentiates pacing artifacts from QRS complexes, even when superimposed.
- Demonstrated significantly improved performance compared to existing ECG computer methods.
- Enabled accurate calculation of ST segment amplitude and slope.
- Provided comprehensive data on pacing and cardiac electrophysiology.
Conclusions:
- The developed algorithm provides a reliable method for interpreting ECGs during transesophageal atrial pacing.
- This advancement enhances the utility of transesophageal atrial pacing for cardiac stress testing in challenging patient populations.
- The algorithm offers a significant improvement over conventional computer-based ECG analysis in this context.
Abstract:
It has been estimated that 15 to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to a variety of reasons such as obesity, poor physical condition, claudication, etc. Transesophageal atrial pacing has been proposed as a noninvasive alternative for inducing cardiac stress in patients who cannot exercise. Although computer analysis is commonly employed to analyze the electrocardiogram (ECG) during the conventional exercise stress test, the surface ECG recorded during transesophageal atrial pacing is contaminated with large pacing artifacts which confound beat identification by standard computer software. We report the development of a robust signal processing algorithm for interpretation of the surface ECG during transesophageal atrial pacing stress. The algorithm employs novel schemes using both linear and nonlinear transformations to detect and differentiate between the pacing artifact and QRS complex even in difficult situations where the pacing artifact is in proximity to or superimposed on the QRS complex. The algorithm uses sophisticated logic for automatic recognition of sustained capture. It subsequently calculates beat-by-beat and average (over five beats) ST segment amplitude and slope. The algorithm also reports the instantaneous heart rate, RR interval, pace-to-R interval, R-wave amplitude, and estimated sinus node recovery time upon loss of sustained capture. The limitations of present exercise ECG computer methods in processing the ECG during transesophageal atrial pacing stress are evaluated and significantly improved performance by our algorithm is demonstrated.
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