Geoffrey C. Green

Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients: a pilot study.

Objective:Our aim is to better characterize the impact of sedation and its interruption on continuously monitored heart rate variability and respiratory rate variability in critically ill patients. We aim to explore whether sedation reduces heart rate variability and respiratory rate variability in critically ill patients and whether the extent of reduction depends on degree of organ dysfunction. Design:Prospective observational pilot study. Setting:Intensive care unit in tertiary care teaching hospital. Patients:Thirty-three critically ill adult patients experiencing respiratory and/or cardiac failure. Interventions:Electrocardiogram and end-tidal capnography waveform capture were initiated from admission or intubation, respectively, and continued to intensive care unit discharge or a maximum of 14 d. Measurements and Main Results:All patient days with a sedation interruption (defined as cessation of a continuous infusion of sedation agent) were identified. Mean heart rate variability and respiratory rate variability were computed over two periods: 4 hrs directly prior to the sedation interruption, and the duration of sedation interruption (median: 1 hr 45 mins, interquartile range: 4 hrs 15 mins or max 4 hrs). Severity of organ dysfunction was assessed through multiple organ dysfunction syndrome scores, and sedative agents were recorded for each sedation interruption. Multiple organ dysfunction syndrome levels were defined as low (0–2), medium (3–4), and high (> 4). Variability before and during sedation interruption was compared and analyzed across multiple organ dysfunction syndrome levels and sedative types. Our results suggest that both heart rate variability and respiratory rate variability increased during sedation interruption (p < 0.05 for coefficient of variation). Patients with low and medium multiple organ dysfunction syndrome experienced greater increase in heart rate variability during sedation interruption (p < 0.05 for coefficient of variation), compared to patients with high multiple organ dysfunction syndrome, who failed to mount a significant increase in heart rate variability when sedation was stopped. Similarly, sedation interruption led to increased respiratory rate variability for low multiple organ dysfunction syndrome patients (p < 0.05 for SD), but in contrast, a further deterioration in respiratory rate variability occurred in the high multiple organ dysfunction syndrome patients. All trends persisted when controlling for sedative agents. Conclusions:Interruption of sedation allows for uncovering a greater restoration of heart rate variability and respiratory rate variability in patients with low organ failure. The further reduction in respiratory variability during the elimination of sedation in patients with high multiple organ dysfunction syndrome suggests a differential response and benefit from sedation interruption, and merits further investigation. As reduced variability correlates with severity of illness, and need for sedation depends on organ failure, variability monitoring may offer a dynamic measure of a variable response to the benefit, timing, and duration of sedation interruption.

Continuous Multiorgan Variability monitoring in critically ill patients — Complexity science at the bedside

Complex systems science has led to valuable insights regarding the care and understanding of critical illness, but has not led to fundamental improvements to care to date. Realizing the fact that there is inherent uncertainty in patient trajectory, we have developed Continuous Individual Multiorgan Variability Analysis (CIMVA) as a tool theoretically and practically designed to track the systemic emergent properties of the host response to injury or infection. We present an overview of CIMVA software, and discuss four separate potential clinical applications that we are evaluating; including early detection of infection, better prediction of extubation failure, continuous monitoring of severity of illness in the ICU, and the evaluation of cardiopulmonary fitness. Future challenges are discussed in conclusion.

Identification of Listeria Species Using a Low-Cost Surface-Enhanced Raman Scattering System With Wavelet-Based Signal Processing

We investigated the ability to distinguish between six species within the Listeria genus (including the human pathogen Listeria monocytogenes ) based on a bacteria samples surface-enhanced Raman scattering (SERS) spectrum. Our measurement system consists of a portable low-cost Raman spectral acquisition unit and associated signal processing and classification modules. First, Listeria was cultured and then adsorbed onto silver colloidal nanoparticles for SERS measurements. A total of 483 SERS spectra were collected and preprocessed (using a stationary wavelet transform decomposition) to remove noise and baseline artifact. Distinguishing features were extracted by retaining detail wavelet coefficients of significant value across multiple scales. Using a linear classifier in association with ldquoleave one outrdquo cross-validation, the system achieved maximum classification accuracies of 96.1% (six-category) and 97.9% (two-category, L. monocytogenes versus all others). Dimensionality reduction was used to decrease the number of features from 74 to 5 while maintaining similar classification accuracy. In the future, it is envisioned that a measurement system such as this, which is a combination of low-cost hardware with sophisticated signal processing, could play a complementary role with existing methods in realizing a rapid inexpensive means of identifying food-borne bacterial pathogens.

An Investigation into the Suitability of Using Three Electronic Nose Instruments for the Detection and Discrimination of Bacteria Types

The use of electronic nose (e-nose) technology for detection of food-borne bacteria has several practical advantages over current laboratory procedures, such as lower cost and reduced testing time. In this work, we are interested in using electronic nose systems to detect E. coli and Listeria in a nutrient broth, and discriminate between these bacteria types at various concentrations. To do this, we use instruments based on three different technologies - fingerprint mass spectrometry, metal oxide sensors, and conductive polymer sensors. Our results indicate that separation between groups can be achieved. We describe the relative merits and drawbacks of each technology and discuss how this rich multimodal dataset can be used to build a classification system

Comparison of heart and respiratory rate variability measures using an intermittent incremental submaximal exercise model

To better understand the alterations in cardiorespiratory variability during exercise, the present study characterized the patterns of change in heart rate variability (HRV), respiratory rate variability (RRV), and combined cardiorespiratory variability (HRV-RRV) during an intermittent incremental submaximal exercise model. Six males and six females completed a submaximal exercise protocol consisting of an initial baseline resting period followed by three 10-min bouts of exercise at 20%, 40%, and 60% of maximal aerobic capacity (V̇O2max). The R-R interval and interbreath interval variability were measured at baseline rest and throughout the submaximal exercise. A group of 93 HRV, 83 RRV, and 28 HRV-RRV measures of variability were tracked over time through a windowed analysis using a 5-min window size and 30-s window step. A total of 91 HRV measures were able to detect the presence of exercise, whereas only 46 RRV and 3 HRV-RRV measures were able to detect the same stimulus. Moreover, there was a loss of overall HRV and RRV, loss of complexity of HRV and RRV, and loss of parasympathetic modulation of HRV (up to 40% V̇O2max) with exercise. Conflicting changes in scale-invariant structure of HRV and RRV with increases in exercise intensity were also observed. In summary, in this simultaneous evaluation of HRV and RRV, we found more consistent changes across HRV metrics compared with RRV and HRV-RRV.

Monitoring of food spoilage with electronic nose: potential applications for smart homes

In ambient-assisted living environments, advanced sensors are used to detect potential problems that may affect the occupant. For a range of unsafe living conditions, characteristic odours arise that can provide early warning of a problem in the dwelling. In this paper, we investigate the concept of smell monitoring in the smart home environment, with particular attention paid to food spoilage. Using a commercially available electronic nose (e-nose) based on a metal-oxide sensor array, the odours associated with five common foods were captured over a seven day period. All foods were readily discriminated at the beginning of the measurement period. However, as the food spoiled, the odour profiles changed significantly. In several cases, the changes for a given food exhibited a clear trajectory in the PCA space. This preliminary work suggests that e-nose technology is a promising candidate for incorporation in the smart home. For widespread adoption, however, future e-nose development must continue to improve current shortcomings such as instability, user intervention, and high cost.

Identification of food spoilage in the smart home based on neural and fuzzy processing of odour sensor responses

Adopting the use of real-time odour monitoring in the smart home has the potential to alert the occupant of unsafe or unsanitary conditions. In this paper, we measured (with a commercial metal-oxide sensor-based electronic nose) the odours of five household foods that had been left out at room temperature for a week to spoil. A multilayer perceptron (MLP) neural network was trained to recognize the age of the samples (a quantity related to the degree of spoilage). For four of these foods, median correlation coefficients (between target values and MLP outputs) of R > 0.97 were observed. Fuzzy C-means clustering (FCM) was applied to the evolving odour patterns of spoiling milk, which had been sampled more frequently (4h intervals for 7 days). The FCM results showed that both the freshest and oldest milk samples had a high degree of membership in “fresh” and “spoiled” clusters, respectively. In the future, as advancements in electronic nose development remove the present barriers to acceptance, signal processing methods like those explored in this paper can be incorporated into odour monitoring systems used in the smart home.

Spatially adaptive wavelet thresholding of rubidium-82 cardiac PET images

/sup 82/Rb positron emission tomography (PET) images can be used to diagnose coronary artery disease (CAD), but deterioration of the images due to noise can compromise their quality. This work presents our work on a wavelet-based thresholding method for denoising cardiac /sup 82/Rb PET images. Our approach is based on a three-dimensional (3D) discrete dyadic wavelet transform, with iterative thresholding of coefficients being performed in a spatially adaptive context. A subjective analysis indicates that this technique leads to better discrimination between diagnostically significant features and noise when observing the denoised images and the corresponding cardiac polar maps. Results are presented for cardiac PET images of a normal subject (low risk of CAD) using a figure of merit based on expected image properties for such a patient. The increase in image quality is substantial when the proposed method is compared to conventional denoising protocols.