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Automatic Pulse Classification for Artefact Removal Using SAX Strings, a CENTER-TBI Study.

Manuel Cabeleira1,2, Marta Fedriga3,4,5, Peter Smielewski3,4

  • 1Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. mc916@cam.ac.uk.

Acta Neurochirurgica. Supplement
|April 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using Symbolic Aggregate Approximation (SAX) to detect artifacts in patient monitoring data. This technique efficiently classifies physiological signals, improving upon manual methods.

Keywords:
Artefact detectionPhysiological signal processingSAX stringsSVM classifiers

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • High-resolution bedside monitor data offers physiological insights but contains artifacts.
  • Manual artifact detection is laborious, error-prone, and unsuitable for real-time analysis.

Purpose of the Study:

  • To develop an automated technique for detecting and eliminating artifacts in physiological waveform data.
  • To improve the efficiency and reliability of artifact detection in patient monitoring.

Main Methods:

  • A novel approach utilizing Symbolic Aggregate Approximation (SAX) to represent physiological pulses as 'words'.
  • Classification of these 'words' as artefactual or physiological using a Support Vector Machine (SVM) classifier.
  • Training the SVM on manually validated arterial blood pressure pulses from 50 patients.

Main Results:

  • The developed algorithm achieved high sensitivity (0.972 and 0.954) and good specificity (0.837) in classifying clean versus artefactual pulses.
  • The SAX-based SVM approach demonstrated effectiveness in a balanced dataset.
  • Independent evaluation by two observers confirmed the algorithm's performance.

Conclusions:

  • The proposed SAX-based SVM technique offers an effective and automated solution for artifact detection in physiological signals.
  • This method has the potential to replace time-consuming manual artifact marking in real-time patient monitoring.
  • The study highlights the utility of SAX and SVM in biomedical signal processing.