Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence

Abstract

Great effort has been devoted in recent years to the development of sudden cardiac risk predictors as a function of electric cardiac signals, mainly obtained from the electrocardiogram (ECG) analysis. But these prediction techniques are still seldom used in clinical practice, partly due to its limited diagnostic accuracy and to the lack of consensus about the appropriate computational signal processing implementation. This paper addresses a three-fold approach, based on ECG indices, to structure this review on sudden cardiac risk stratification. First, throughout the computational techniques that had been widely proposed for obtaining these indices in technical literature. Second, over the scientific evidence, that although is supported by observational clinical studies, they are not always representative enough. And third, via the limited technology transfer of academy-accepted algorithms, requiring further meditation for future systems. We focus on three families of ECG derived indices which are tackled from the aforementioned viewpoints, namely, heart rate turbulence (HRT), heart rate variability (HRV), and T-wave alternans. In terms of computational algorithms, we still need clearer scientific evidence, standardizing, and benchmarking, siting on advanced algorithms applied over large and representative datasets. New scenarios like electronic health recordings, big data, long-term monitoring, and cloud databases, will eventually open new frameworks to foresee suitable new paradigms in the near future.

Keywords: T–wave alternans; computational algorithms; heart rate turbulence; heart rate variability; risk stratification; scientific evidence; sudden cardiac death; technology transfer.

Figure 1

Biphasic response after a VPB in an RR interval time series (circles). The computation of both TO and TS is also represented.

Figure 2

Example of HRV signal tachogram, or signal given by the times elapsed between consecutive beats as a function of the beat number (A), and the analysis with Lorentz plot (B), power spectral density (C), and nonlinear techniques in terms of detrended fluctuation analysis (D).

Figure 3

Illustrative example of TWA. (A) ECG signal with periodic pattern alternation in the repolarization segment with a period of two beats. (B) Visual interpretation of TWA as the difference between the averaged event beat and the averaged odd beat.

Figure 4

Number of issued patents by year at European Patent Office (adapted from Espacenet).