Automated QT interval measurement using modified Pan-Tompkins algorithm with independent isoelectric line approach

• Main Authors: Jumahat, Shaliza, Gan, Kok Beng, Misran, Norbahiah, Islam, Mohammad Tariqul, Mahri, Nurhafizah, Ja'afar, Mohd. Hasni
• Format: Article
• Language: English; English
• Published: Trans Tech Publications 2020
• Subjects: TA164 Bioengineering; TA168 Systems engineering
• Online Access: http://irep.iium.edu.my/79098/7/79098%20Automated%20QT%20Interval%20Measurement%20Using%20Modified%20Pan-Tompkins.pdf; http://irep.iium.edu.my/79098/8/79098%20Automated%20QT%20Interval%20Measurement%20Using%20Modified%20Pan-Tompkins%20SCOPUS.pdf; http://irep.iium.edu.my/79098/; https://www.scientific.net/JBBBE.44.51

The QT interval on the electrocardiogram (ECG) signal is known to have an important role in monitoring heart’s electrical activity because the presence of QT interval prolongation can be associated with life-threatening cardiac events. This interval can be identified and measured using either manual or automated techniques. Currently, studies on automated QT interval measurement algorithms are becoming a growing field, as they can provide the best solution to overcome misdiagnosis and timely issues resulting from manual identification. However, the physiological variability of the QRS complex and the fluctuation of the isoelectric line are prevalent issues that need to be considered in the automatic method. In this report, an algorithm to identify the QRS onset and T-wave offset for measuring the corrected QT interval (QTc) is proposed. This method uses an improved Pan-Tompkins algorithm from the previous work with independent of the isoelectric line approach for detecting the QRS onset and the T offset. The algorithm was implemented in Matlab and applied to the 60 seconds duration of 27 records in the PPUKM database with a sampling frequency of 500 Hz. The performance of the algorithm achieved a sensitivity of 100% for QRS onset detection and 100% for T offset detection. As for the accuracy, the algorithm’s performance obtained 100% for QRS onset detection and 99.56% for T offset detection. The mean error results with respect to manual annotation were 37±18.5 ms for QRS onset detection and 32±22.3 ms for T offset detection which was within ANSI/AAMI-EC57:1998 standard tolerance. The proposed algorithm exhibits reliable automated QTc measurement. Besides insensitive to morphological variations of ECG waves, the computational method is simple and possibly implemented as the basis for future software development for portable device applications.