Signal quality assessment of peripheral venous pressure

Neng Tai Chiu, Beau Chuang, Suthawan Anakmeteeprugsa, Kirk H. Shelley, Aymen Awad Alian, Hau Tieng Wu

Research output: Contribution to journalArticlepeer-review


Develop a signal quality index (SQI) for the widely available peripheral venous pressure waveform (PVP). We focus on the quality of the cardiac component in PVP. We model PVP by the adaptive non-harmonic model. When the cardiac component in PVP is stronger, the PVP is defined to have a higher quality. This signal quality is quantified by applying the synchrosqueezing transform to decompose the cardiac component out of PVP, and the SQI is defined as a value between 0 and 1. A database collected during the lower body negative pressure experiment is utilized to validate the developed SQI. All signals are labeled into categories of low and high qualities by experts. A support vector machine (SVM) learning model is trained for practical purpose. The developed signal quality index coincide with human experts’ labels with the area under the curve 0.95. In a leave-one-subject-out cross validation (LOSOCV), the SQI achieves accuracy 0.89 and F1 0.88, which is consistently higher than other commonly used signal qualities, including entropy, power and mean venous pressure. The trained SVM model trained with SQI, entropy, power and mean venous pressure could achieve an accuracy 0.92 and F1 0.91 under LOSOCV. An exterior validation of SQI achieves accuracy 0.87 and F1 0.92; an exterior validation of the SVM model achieves accuracy 0.95 and F1 0.96. The developed SQI has a convincing potential to help identify high quality PVP segments for further hemodynamic study. This is the first work aiming to quantify the signal quality of the widely applied PVP waveform.

Original languageEnglish (US)
Pages (from-to)101-112
Number of pages12
JournalJournal of Clinical Monitoring and Computing
Issue number1
StatePublished - Feb 2024


  • Lower body negative pressure
  • Peripheral venous pressure
  • Signal quality index
  • Synchrosqueezing transform

ASJC Scopus subject areas

  • Health Informatics
  • Critical Care and Intensive Care Medicine
  • Anesthesiology and Pain Medicine


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