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

Overview of attention for article published in BioMedical Engineering OnLine, May 2018
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Title
A new approach for analysis of heart rate variability and QT variability in long-term ECG recording
Published in
BioMedical Engineering OnLine, May 2018
DOI 10.1186/s12938-018-0490-8
Pubmed ID
Authors

Hau-Tieng Wu, Elsayed Z. Soliman

Abstract

With 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. In 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. As 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. The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 18%
Researcher 4 12%
Student > Master 3 9%
Student > Bachelor 2 6%
Lecturer 2 6%
Other 6 18%
Unknown 11 32%
Readers by discipline Count As %
Engineering 7 21%
Medicine and Dentistry 4 12%
Sports and Recreations 2 6%
Computer Science 2 6%
Nursing and Health Professions 1 3%
Other 6 18%
Unknown 12 35%