Journal of Clinical Monitoring and Computing 

About: Journal of Clinical Monitoring and Computing is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Intensive care & Medicine. It has an ISSN identifier of 1387-1307. Over the lifetime, 3351 publications have been published receiving 53797 citations.

A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques.

Abstract: Introduction. Bias and precision statistics have succeeded regression analysis when measurement techniques are compared. However, when applied to cardiac output measurements, inconsistencies occur in reporting the results of this form of analysis. Methods. A MEDLINE search was performed, dating from 1986. Studies comparing techniques of cardiac output measurement using bias and precision statistics were surveyed. An error-gram was constructed from the percentage errors in the test and reference methods and was used to determine acceptable limits of agreement between methods. Results. Twenty-five articles were found. Presentation of statistical data varied greatly. Four different statistical parameters were used to describe the agreement between measurements. The overall limits of agreement in studies evaluating bioimpedance (n = 23) was ±37% (15–82%) and in those evaluating Doppler ultrasound (n = 11) ±65% (25–225%). Objective criteria used to assess outcome were given in only 44% of the articles. These were (i) limits of agreement approaching ±15–20%, (ii) limits of agreement of less than 1 L/min, and (iii) more than 75% of bias measurements within ±20% of the mean. Graphically, we showed that limits of agreement of up to ±30% were acceptable. Conclusions. When using bias and precision statistics, cardiac output, bias, limits of agreement, and percentage error should be presented. Using current reference methods, acceptance of a new technique should rely on limits of agreement of up to ±30%.

An introduction to bispectral analysis for the electroencephalogram

Abstract: The goal of much effort in recent years has been to provide a simplified interpretation of the electroencephalogram (EEG) for a variety of applications, including the diagnosis of neurological disorders and the intraoperative monitoring of anesthetic efficacy and cerebral ischemia. Although processed EEG variables have enjoyed limited success for specific applications, few acceptable standards have emerged. In part, this may be attributed to the fact that commonly usedsignal processing tools do not quantify all of the information available in the EEG. Power spectral analysis, for example, quantifies only power distribution as a function offrequency, ignoring phase information. It also makes the assumption that thesignal arises from alinear process, thereby ignoring potential interaction betweencomponents of the signal that are manifested asphase coupling, a common phenomenon in signals generated fromnonlinear sources such as the central nervous system (CNS). This tutorial describes bispectral analysis, a method of signal processing that quantifies the degree of phase coupling between the components of a signal such as the EEG. The basic theory underlying bispectral analysis is explained in detail, and information obtained from bispectral analysis is compared with that available from thepower spectrum. The concept of abispectral index is introduced. Finally, several model signals, as well as a representative clinical case, are analyzed using bispectral analysis, and the results are interpreted.

A regularity statistic for medical data analysis.

Abstract: A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

Monitoring tissue oxygenation by near infrared spectroscopy (NIRS): background and current applications

Abstract: Conventional cardiovascular monitoring may not detect tissue hypoxia, and conventional cardiovascular support aiming at global hemodynamics may not restore tissue oxygenation. NIRS offers non-invasive online monitoring of tissue oxygenation in a wide range of clinical scenarios. NIRS monitoring is commonly used to measure cerebral oxygenation (rSO(2)), e.g. during cardiac surgery. In this review, we will show that tissue hypoxia occurs frequently in the perioperative setting, particularly in cardiac surgery. Therefore, measuring and obtaining adequate tissue oxygenation may prevent (postoperative) complications and may thus be cost-effective. NIRS monitoring may also be used to detect tissue hypoxia in (prehospital) emergency settings, where it has prognostic significance and enables monitoring of therapeutic interventions, particularly in patients with trauma. However, optimal therapeutic agents and strategies for augmenting tissue oxygenation have yet to be determined.

Masimo signal extraction pulse oximetry.

Abstract: Objective.To describe a new pulse oximetry technology and measurement paradigm developed by Masimo Corporation.Introduction.Patient motion, poor tissue perfusion, excessive ambient light, and electrosurgical unit interference reduce conventional pulse oximeter (CPO) measurement integrity. Patient motion frequently generates erroneous pulse oximetry values for saturation and pulse rate. Motion-induced measurement error is due in part to wide spread implementation of a theoretical pulse oximetry model which assumes that arterial blood is the only light-absorbing pulsatile component in the optical path. Methods.Masimo Signal Extraction Technology(SET®) pulse oximetry begins with conventional red and infrared photoplethysmographic signals, and then employs a constellation of advanced techniques including radiofrequency and light-shielded optical sensors, digital signal processing, and adaptive filtration, to measure SpO2 accurately during challenging clinical conditions. In contrast to CPO which calculates O2 saturation from the ratio of transmitted pulsatile red and infrared light, Masimo SET pulse oximetry uses a new conceptual model of light absorption for pulse oximetry and employs the discrete saturation transform (DST) to isolate individual “saturation components” in the optical pathway. Typically, when the tissue under analysis is stationary, only the single saturation component produced by pulsatile arterial blood is present.In contrast, during patient motion, movement of non-arterial components (for example, venous blood) can be identified as additional saturation components (with a lower O2 saturation). When conditions of the Masimo model are met, the saturation component corresponding to the highest O2 saturation is reported by the instrument as SpO2. Conclusion.The technological strategies implemented in Masimo SET pulse oximetry effectively permit continuous monitoring of SpO2 during challenging clinical conditions of motion and poor tissue perfusion.