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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...
Discrete Fourier Transform01:15

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...

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Related Experiment Video

Updated: May 30, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
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Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

A continuous wavelet transform-based method for time-frequency analysis of artefact-corrected heart rate variability

C H L Peters1, R Vullings, M J Rooijakkers

  • 1Department of Clinical Physics, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands. c.peters@jbz.nl

Physiological Measurement
|August 19, 2011
PubMed
Summary

A new continuous wavelet transform (CWT) method improves time-frequency analysis of heart rate variability (HRV) by excluding artefact-affected data, offering more accurate spectral estimates for clinical insights.

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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Published on: June 5, 2019

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Time-frequency analysis of heart rate variability (HRV) is clinically relevant but sensitive to signal artefacts.
  • Artefact correction in HRV data can reduce signal variability, compromising spectral estimate accuracy.

Purpose of the Study:

  • To develop and evaluate a novel continuous wavelet transform (CWT)-based method for artefact-resilient HRV time-frequency analysis.
  • To overcome the limitations of traditional methods in handling artefacts in HRV recordings.

Main Methods:

  • A new CWT-based method was developed, excluding artefact-corrupted scalogram portions from power calculations.
  • The method was validated using simulated artefact correction on HRV data.
  • Comparison with the short-time Fourier transform (STFT) as a reference method.

Main Results:

  • STFT-based spectral estimates were highly sensitive to artefact correction, except for very low-frequency and low-frequency (LF) bands.
  • CWT-based calculations in high-frequency and very high-frequency bands closely matched theoretical values.
  • LF band power showed acceptable sensitivity up to 20% artefact correction.

Conclusions:

  • The CWT-based method offers a robust alternative for HRV time-frequency analysis, particularly when data may contain artefacts.
  • This approach enhances the reliability of spectral estimates in clinical HRV analysis.