Electrocardiogram-based parameters for the prediction of sudden cardiac death: a review

There has recently been a resurgence of interest in electrocardiogram-based (ECG-based) parameters in predicting Sudden Cardiac Death (SCD) risk. Accurate and timely SCD predictions are essential clinical practice for physicians to provide effective prevention and treatment. An ECG is a non-invasive and inexpensive diagnostic test, and has been firmly established as a clinical tool for assessing the risk of cardiac disease. The electrocardiographic signal derived from the ECG recording consists of a distinctive waveform that depicts the electrical activity of the heart, which can be analyzed for the identification of abnormalities in the heart rhythm. The parameter or characteristic found in the ECG signal might be important for predicting the SCD. A number of systematic reports by expert meetings and review articles in indexed journals identified ECG-based parameters as QRS duration, QT interval, Signal Average ECG (SAECG), T-wave alternan (TWA), Heart Rate Variability (HRV), Heart Rate Turbulence (HRT), T-peak to T-end (Tpe), fragmented QRS complexes (fQRS), and Early Repolarization (ER). This article reviews the mechanism and morphology of these parameters, which may potentially have a role to play in a future algorithm designed to identify early signs of SCD. As of now, none of the ECG-based parameters have been found to be sufficiently stable to predict the SCD risk. Nevertheless, the combination of two or more of the parameters listed, as suggested in many studies, may become a useful component for predicting SCD in the future

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

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