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Related Concept Videos

Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...

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Haemosync: A synchronisation algorithm for multimodal haemodynamic signals.

Nick Eleveld1, Marije Harmsen1, Jan Willem J Elting1

  • 1University of Groningen, University Medical Center Groningen, Department of Neurology, 9713 GZ Groningen, the Netherlands.

Computer Methods and Programs in Biomedicine
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately detects and corrects time-shifts in arterial blood pressure (ABP) and cerebral blood velocity (CBv) signals, improving multimodal analysis for dynamic cerebral autoregulation (DCA). This ensures reliable haemodynamic signal synchronisation.

Keywords:
Arterial blood pressureCerebral blood velocityHemodynamic signalsPulsatile signalsSynchronizationTime-shift

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Medical Signal Processing

Background:

  • Synchronous acquisition of haemodynamic signals like arterial blood pressure (ABP) and cerebral blood velocity (CBv) is vital for multimodal analysis, including dynamic cerebral autoregulation (DCA).
  • Technical issues can introduce time-shifts between these signals, complicating analysis and potentially skewing results.
  • Accurate signal synchronisation is essential for reliable interpretation of physiological data.

Purpose of the Study:

  • To develop and validate a novel algorithm for detecting and correcting time-shifts in multimodal pulsatile haemodynamic signals.
  • To assess the algorithm's performance under various conditions, including different time-shift magnitudes, noise levels, and waveform variations.
  • To evaluate the impact of time-shift correction on the accuracy of dynamic cerebral autoregulation (DCA) indices.

Main Methods:

  • A multistep, cross-correlation-based algorithm was developed for time-shift detection and synchronization of ABP and CBv signals.
  • The algorithm was trained and validated on datasets with known time-shifts, including gradual drifts and sudden shifts.
  • Quantitative validation included assessing performance with artificially introduced time-shifts (-4 to 4 seconds) and varying noise levels.

Main Results:

  • The algorithm achieved a median absolute error of 12 ms for time-shift estimation across various conditions, especially when a peak cross-correlation threshold (>0.9) was applied.
  • Time-shift estimation demonstrated robustness to superimposed white noise.
  • Post-correction, dynamic cerebral autoregulation (DCA) indices showed minimal differences compared to original, non-time-shifted signals, with small phase shifts observed.

Conclusions:

  • The developed algorithm provides a reliable method for visually interpretable detection and accurate correction of time-shifts between ABP and CBv signals.
  • This tool enhances the quality of multimodal haemodynamic signal analysis, particularly for dynamic cerebral autoregulation (DCA) studies.
  • Accurate signal synchronization is crucial for advancing the understanding of cerebrovascular function.