• Anesthesia and analgesia · Dec 2014

    Review

    A review of signal processing used in the implementation of the pulse oximetry photoplethysmographic fluid responsiveness parameter.

    • Paul S Addison.
    • From the Respiratory and Monitoring Solutions, Covidien, The Technopole Centre, Edinburgh, Scotland, United Kingdom.
    • Anesth. Analg.. 2014 Dec 1;119(6):1293-306.

    AbstractΔPOP is a physiological parameter derived from the respiration-induced change in the pulse oximetry plethysmographic (POP) waveform or "pleth." It has been proposed as a proxy for pulse pressure variation used in the determination of the response to intravascular volume expansion in hypovolemic patients. Many studies have now reported on the parameter, and many research groups have constructed algorithms for its computation from the first principles where the implementation details have been described. This review focuses on the signal processing aspects of ΔPOP, as reported in the literature, and aims to provide a comprehensive summary of the wide-ranging algorithmic strategies that have been attempted in its computation. A search was conducted for articles concerning the use of ΔPOP as a fluid responsiveness parameter. In particular, articles concerning the correlation between ΔPOP and pulse pressure variation were targeted. Comments and replies to comments by the authors in which signal processing aspects were discussed were also included in the review. The parameter is first defined, and a history of the early work surrounding pleth-based fluid responsiveness parameters is presented. This is followed by an overview of the signal processing methods used in the reported studies, including details of exclusion criteria, manual filtering (preprocessing), gain change issues, acquisition details, selection of registration periods, averaging methods, physiological influences on the pleth, and comments by the investigators themselves. It is concluded that to develop a robust, fully automated ΔPOP algorithm for use in the clinical environment, more rigorous signal processing is required. Specifically, signals should be evaluated over significant periods of time, with emphasis on the quality and temporal relevance of the information.

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