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

Richard L Verrier

doi:10.3389/fphys.2016.00267

Abstract

I read with interest the excellent article on sudden cardiac death risk stratification with electrocardiographic (ECG) indices by Dr. Gimeno-Blanes and his colleagues in the recent issue of Frontiers in Physiology (Gimeno-Blanes et al., 2016). The article is unique in that it included computational processing, technology transfer, and scientific evidence, critical considerations in the long and challenging path from bench to bedside. The parameters discussed, namely heart rate variability, heart rate turbulence, and microvolt T-wave alternans (TWA), are well-considered as they are the most extensively studied contemporary ECG-based risk stratification parameters.

Highlights

Harvard Thorndike Integrative Cardiac Electrophysiology Laboratory, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
I read with interest the excellent article on sudden cardiac death risk stratification with electrocardiographic (ECG) indices by Dr Gimeno-Blanes and his colleagues in the recent issue of Frontiers in Physiology (Gimeno-Blanes et al, 2016)
I would like to register some additional considerations with respect to time-domain analysis of microvolt T-wave alternans (TWA) using the Modified Moving Average (MMA) method

Summary

Introduction

Harvard Thorndike Integrative Cardiac Electrophysiology Laboratory, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence by Gimeno-Blanes, F. I read with interest the excellent article on sudden cardiac death risk stratification with electrocardiographic (ECG) indices by Dr Gimeno-Blanes and his colleagues in the recent issue of Frontiers in Physiology (Gimeno-Blanes et al, 2016).

Results

Conclusion