Key result
A novel automated QT-interval analysis method combining Continuous Wavelet Transform and the Philips algorithm achieved a mean error of 12.89 ms and standard deviation of 9.76 ms on the entire QT database.
Why the study?
Automated QT interval surveillance is critical due to challenges in ventricular repolarization measurement from noise and limitations of manual analysis like fatigue.
Does an automated method combining Continuous Wavelet Transform and Philips method improve QT interval analysis accuracy compared to manual and standard automated methods in ECG recordings?
Does an automated method combining Continuous Wavelet Transform and Philips method improve QT interval analysis accuracy compared to manual and standard automated methods in ECG recordings?
A novel automated algorithm combining Continuous Wavelet Transform and the Philips method provides robust and accurate QT interval analysis compared to standard methods.
Novel QT algorithm may aid noisy ECG analysis; leaves open validation before clinical adoption.
QT surveillance is the most vital appliance to detect the possibility of sudden death sourced by using pro-arrhythmic drugs treating abnormal conditions in the heart. The repolarization of ventricles makes QT interval surveillance difficult since noisy conditions and individual cardiac situations. Besides, an automated QT algorithm is crucial due to a manual QT measurement with some disadvantages such as fatigue condition in reading long records. In this study, a fully novel automated method combining Continuous Wavelet Transform and Philips method was established to perform QT interval analysis. Electrocardiogram recordings were obtained from PhyisoNet database marked by manual and standard automated methods. The proposed algorithm had scores of 15.46 and 11.87 millisecond mean error with 11.85 and 9.91 millisecond standard deviation in terms of gold and silver standards, respectively. Also, the entire QT database was utilized in order to test the algorithm performance with the score of 12.89 and 9.76 millisecond mean and standard deviation errors, respectively. The present algorithm performance had scores of −0.21 ± 7.81 at golden standard, and −4.10 ± 18.21 millisecond error for the whole QT database tests, respectively. The proposed algorithm is attained to more stable and robust results with a higher performance than the previous comparable studies.
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İşcan et al. (2021) studied QT interval surveillance. Automated method combining Continuous Wavelet Transform and Philips method vs. Manual and standard automated methods (gold and silver standards) was evaluated on QT interval analysis mean error and standard deviation. A novel automated QT-interval analysis method combining Continuous Wavelet Transform and the Philips algorithm achieved a mean error of 12.89 ms and standard deviation of 9.76 ms on the entire QT database.
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