Article

Utility of the photoplethysmogram in circulatory monitoring.

Department of Medicine, Division of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Anesthesiology (Impact Factor: 6.17). 06/2008; 108(5):950-8. DOI: 10.1097/ALN.0b013e31816c89e1
Source: PubMed

ABSTRACT The photoplethysmogram is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is displayed by many pulse oximeters and bedside monitors, along with the computed arterial oxygen saturation. The photoplethysmogram is similar in appearance to an arterial blood pressure waveform. Because the former is noninvasive and nearly ubiquitous in hospitals whereas the latter requires invasive measurement, the extraction of circulatory information from the photoplethysmogram has been a popular subject of contemporary research. The photoplethysmogram is a function of the underlying circulation, but the relation is complicated by optical, biomechanical, and physiologic covariates that affect the appearance of the photoplethysmogram. Overall, the photoplethysmogram provides a wealth of circulatory information, but its complex etiology may be a limitation in some novel applications.

6 Followers
 · 
335 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Detecting return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation (CPR) is challenging, timeconsuming, and requires interrupting chest compressions. Based on automated-CPR porcine data, we have developed an algorithm to support ROSC detection, which detects cardiogenic output during chest compressions via a photoplethysmography (PPG) signal. The algorithm can detect palpable and impalpable spontaneous pulses. A compression-free PPG signal which estimates the spontaneous pulse waveform, was obtained by subtracting the compression component, modeled by a harmonic series. The fundamental frequency of this series was the compression rate derived from the trans-thoracic impedance signal measured between the defibrillation pads. The amplitudes of the harmonic components were obtained via a least mean-square algorithm. The frequency spectrum of the compression-free PPG signal was estimated via an autoregressive model, and the relationship between the spectral peaks was analyzed to identify the pulse rate (PR). Resumed cardiogenic output could also be detected from a decrease in the baseline of the PPG signal, presumably caused by a redistribution of blood volume to the periphery. The algorithm indicated cardiogenic output when a PR or a redistribution of blood volume was detected. The algorithm indicated cardiogenic output with 94% specificity and 69% sensitivity compared to the retrospective ROSC detection of nine clinicians. Results showed that ROSC detection can be supported by combining the compression-free PPG signal with an indicator based on the detected PR and redistribution of blood volume.
    IEEE transactions on bio-medical engineering 11/2014; DOI:10.1109/TBME.2014.2370649 · 2.23 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a robust method for pulse peak determination in a digital volume pulse (DVP) waveform with a wandering baseline. A proposed new method uses a modified morphological filter (MMF) to eliminate a wandering baseline signal of the DVP signal with minimum distortion and a slope sum function (SSF) with an adaptive thresholding scheme to detect pulse peaks from the baseline-removed DVP signal. Further in order to cope with over-detected and missed pulse peaks, knowledge based rules are applied as a postprocessor. The algorithm automatically adjusts detection parameters periodically to adapt to varying beat morphologies and fluctuations. Compared with conventional methods (highpass filtering, linear interpolation, cubic spline interpolation, and wavelet adaptive filtering), our method performs better in terms of the signal-to-error ratio, the computational burden (0.125 seconds for one minute of DVP signal analysis with the Intel Core 2 Quad processor @ 2.40 GHz PC), the true detection rate (97.32% with an acceptance level of 4 ms ) as well as the normalized error rate (0.18%). In addition, the proposed method can detect true positions of pulse peaks more accurately and becomes very useful for pulse transit time (PTT) and pulse rate variability (PRV) analyses.
    IEEE Transactions on Biomedical Circuits and Systems 10/2014; 8(5):729-37. DOI:10.1109/TBCAS.2013.2295102 · 3.15 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: ΔPOP 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.
    Anesthesia & Analgesia 12/2014; 119(6):1293-306. DOI:10.1213/ANE.0000000000000392 · 3.42 Impact Factor

Preview

Download
25 Downloads
Available from