A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Hau-Tieng Wu,Elsayed Z Soliman

doi:10.1186/s12938-018-0490-8

Abstract

Background and purposeWith the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.MethodsIn this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.ResultsAs proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.ConclusionThe ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Highlights

Background and purposeWith the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the develop‐ ment of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings
Concentration of frequency and time–time‐varying power spectrum To take into account the non-stationarity of a given time series, such as the physiological non-stationarity status of the HRV time series, and to avoid the limitations encountered in linear-type and quadratic-type Time Frequency (TF) analysis tools, we considered the recently developed nonlinear-type TF analysis technique concentration of frequency and time (ConceFT)
Simulated signal To illustrate the performance of ConceFT, we demonstrated how it works in a simulated multi-component signal, and compared the resulting time-varying power spectrum (tvPS) with those generated by the commonly applied TF analysis tools, like MT synchrosqueezed spectrogram, spectrogram, scalogram, smoothed pseudo Wigner-Ville distribution (SPWVD), and Choi-William distribution (CWD)

Summary

Introduction

Background and purposeWith the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the develop‐ ment of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings. Several methods are currently used to quantify the HRV from 24 to 48 h long-term electrocardiogram (ECG) recordings [1,2,3,4,5]. Most of the methods applied to measure long-term HRV are based on the stationarity assumption [1,2,3, 10], a common assumption in many time series techniques. While those methods could still be applied to any non-stationary time series, such as 24–48 h long-term heart rate, the results might not be directly interpretable, miss the finer non-stationary dynamics, or even misleading [11]. The problem becomes more challenging with “extra-long-term” ECG recordings, such as 7 days or longer [4, 12]

Methods

Results

Discussion

Conclusion