Modeling the weaning of intensive care unit patients from mechanical ventilation: a review.

In the intensive care unit, mechanical ventilation is a life-saving procedure, and as many as 90% of patients require the intervention. For a mechanically ventilated patient, the principal goal of a health care team is to free the patient from mechanical ventilation through weaning as soon as possible. Weaning, however, still is mostly a manual process. To achieve quick and efficient weaning, the process is needs to be automated. The first step toward automating the weaning process is building a precise model of it. The path to achieving this precision in weaning modeling, if at all possible, is laden with challenges such as the use of imprecise terms, lack of evidence, complexities in data representation as well as process specification, and uncertainty in data values as well as their implication in process evaluation. This eventually leads to a lack of universally accepted and followed standards and guidelines. Despite the magnitude of these challenges, various weaning automations have been attempted through mathematical modeling or knowledge-based modeling. Some of these have been available as commercial mechanical ventilator modes since the 1990s. Even though much potential has been demonstrated through clinical trials, their infrequent usage indicates a lack of consensus concerning their applicability.

GERD and obesity: is the autonomic nervous system the missing link?

Gastroesophageal reflux disease (GERD) is a common chronic condition that not only impairs the quality of life of those who are affected by it but also poses a significant economic burden. It encompasses a wide spectrum of symptoms as a result of gastric content moving into the esophagus. The most common cause of GERD, other than a hiatus hernia, is considered to be transient lower esophageal sphincter relaxation. The lower esophageal sphincter (LES) normally has a higher resting tone than the stomach, thus preventing the reflux of gastric contents into the esophagus. The greater prevalence of GERD and GERD symptoms in obese individuals has generated significant interest in understanding the association between these 2 conditions and the underlying physiological mechanisms. The potential relationship between GERD and obesity and the exact mechanism by which obesity may cause reflux, however, remains uncertain. It has been proposed that patients with GERD have altered autonomic nervous function and, more specifically, have reduced parasympathetic activity. Obese individuals also have shown diminished parasympathetic activity, which may be reversed after weight reduction through exercise, diet control, and bariatric surgery. Given that contraction and relaxation of the LES are vagally mediated, the question that arises is whether the autonomic nervous system is, in fact, the missing link between obesity and GERD. In this article we examine the current evidence and hypothesize that the potential imbalance in sympathovagal stimulation to the LES is a key contributing factor to the increased prevalence of GERD symptoms in obese individuals.

A Multi-Step Algorithm for Measuring Airway Luminal Diameter and Wall Thickness in Lung CT Images.

Accurate measurements of airway diameter and wall thickness are important parameters in understanding numerous pulmonary diseases. Here, we describe an automated method of measuring small airway luminal diameter and wall thickness over numerous contiguous computed tomography (CT) images. Using CT lung images from 22 patients and an airway phantom, a seeded region-growing algorithm was first applied to identify the lumen of the airway. The result was applied as an initial region for boundary determination using the level set method. Once found, subsequent algorithmic expansion of the luminal border was used to calculate airway wall thickness. This algorithm automatically evaluates neighboring slices of the airway and measures the airway luminal diameter and wall thickness. This approach also detects airway bifurcations. Our new procedure provides rapid, automated, accurate, and clinically important lung airway measurements that would be useful to radiologists who use CT images for pulmonary disease assessment.

A morphological algorithm for measuring angle of airway branches in lung CT images.

Accurate measurement of human airway lumen bifurcation angle in the bronchial tree may be an important parameter for evidence of pulmonary diseases. Here, we describe a new method for recognizing and following airway bifurcation over numerous contiguous CT images. Based on morphological properties of airways and specific changes to airway properties while digitally navigating through the bifurcation, our method is able to track airways through several levels of bifurcation. Then, based on the center of the lumen area, determined by the level set segmentation algorithm, we estimate the centerline of each branch and calculate the angle between two bifurcating branches. By applying this method to an airway imaging phantom, we obtained accurate results in a short computational time. This new approach provides a rapid, automated, and accurate lung airway angle measurement and may prove useful to radiologists who use CT images for pulmonary disease assessment.

Evaluation of the efficacy and performance of medical implants: a review.

An implant can be defined, in a medical context, as biological or artificial materials inserted or grafted into the body. Implants may be sensory devices (cochlear, ocular), mechanical devices that are 'passive' (orthopedic joint replacements and fixation plates, dental implants, coronary artery stents and vascular grafts) or 'active' (left ventricular assist devices, heart valves) electrophysiological stimulation devices (cardiac or gastric pacemakers, implantable cardiac defibrillators, functional electrical stimulators for epilepsy or Parkinson's disease) or medication administration devices (insulin or analgesic delivery pumps) or intra-ocular sustained drug release implants. Implantation has had a long history in several subspecialties of medicine. Evaluation of the efficacy of implants is a multifactorial issue. Several variables need to be considered while studying the rejection of the implants such as pathophysiological mechanisms, malfunction, design shortcomings and improper implementation/implantation by a medical team. This paper identifies a variety of modes of failure and how they affect the overall efficacy of the device technologies. Suggestions for improvement, as outlined in the literature, will be examined.

Longer-term effects of implanted vagal nerve stimulation.

Vagal nerve stimulation (VNS) is a non-pharmacologic therapeutic intervention approved in adults and children with neuropsychiatric disorders. Studies conducted over the past 20 years have demonstrated that VNS results in immediate and longer-term changes in brain regions implicated in neuropsychiatric disorders, such as the thalamus, cerebellum, orbitofrontal cortex, limbic system, hypothalamus, and medulla with vagus innervations. This review summarizes the effects of longer-term implanted VNS and how the incorporation of this non-pharmacologic therapeutic management in the treatment regime can be beneficial to address the needs of patients who are unable to tolerate medications and/or undergo surgery and do not respond to pharmacologic therapies. We also highlight the therapeutic efficacy of longer-term implanted VNS, safety, tolerability, patient acceptance, adherence, and adverse events, if any, in adults and children in this modality of treatment.

Advances in multimodality imaging through a hybrid PET/MRI system.

The development of integrated imaging systems for magnetic resonance imaging (MRI) and positron emission tomography (PET) is currently being explored in a number of laboratories and industrial settings. PET/MRI scanners for both preclinical and human research applications are being developed. PET/MRI overcomes many limitations of PET/computed tomography (CT), such as limited tissue contrast and high radiation doses delivered to the patient or the animal being studied. In addition, recent PET/MRI designs allow for simultaneous rather than sequential acquisition of PET and MRI data, which could not have been achieved through a combination of PET and CT scanners. In a combined PET/CT scanner, while both scanners share a common patient bed, they are hard-wired back-to-back and therefore do not allow simultaneous data acquisition. While PET/MRI offers the possibility of novel imaging strategies, it also creates considerable challenges for acquiring artifact-free images from both modalities. In this review, we discuss motivations, challenges, and potential research applications of developing PET/MRI technology. A brief overview of both MRI and PET is presented and preclinical and clinical applications of PET/MRI are identified. Finally, issues and concerns about image quality, clinical practice, and economic feasibility are discussed.

Segmentation of images of abdominal organs.

Abdominal organ segmentation, which is, the delineation of organ areas in the abdomen, plays an important role in the process of radiological evaluation. Attempts to automate segmentation of abdominal organs will aid radiologists who are required to view thousands of images daily. This review outlines the current state-of-the-art semi-automated and automated methods used to segment abdominal organ regions from computed tomography (CT), magnetic resonance imaging (MEI), and ultrasound images. Segmentation methods generally fall into three categories: pixel based, region based and boundary tracing. While pixel-based methods classify each individual pixel, region-based methods identify regions with similar properties. Boundary tracing is accomplished by a model of the image boundary. This paper evaluates the effectiveness of the above algorithms with an emphasis on their advantages and disadvantages for abdominal organ segmentation. Several evaluation metrics that compare machine-based segmentation with that of an expert (radiologist) are identified and examined. Finally, features based on intensity as well as the texture of a small region around a pixel are explored. This review concludes with a discussion of possible future trends for abdominal organ segmentation.

The effect of depression, anxiety and early life trauma on the cortisol awakening response during pregnancy: preliminary results.

The purpose of this study was to examine the effects of maternal depression and anxiety on the cortisol awakening response (CAR), a marker of the hypothalamic-pituitary-adrenal (HPA) axis function, during pregnancy. Sixty-six pregnant women were studied between 25 and 33 weeks of gestation and were identified as either Depressed (n=33) or healthy, Control (n=33), based on depression scores and lifetime psychiatric history. Saliva samples were collected (passive drool) upon awakening and at +30 and +60 min thereafter. The CAR was not significantly different between women who were depressed during pregnancy compared to healthy control women. However, women taking antidepressant (AD) medication showed an attenuated CAR (time x AD use interaction, p=0.06). Childhood maltreatment (as measured with the Childhood Trauma Questionnaire) was associated with a lower baseline cortisol concentration explaining 12% of the variance, controlling for wake-up time and AD use. There is a complex interplay of factors involved in the HPA axis regulation of vulnerable women during pregnancy, including depression, anxiety, early life stress and psychotropic medication use, which remain unclear. The CAR may provide important information about the maternal HPA axis during pregnancy and warrants further investigation in larger cohorts.

Deep Brain Stimulation as a Treatment for Refractory Epilepsy: Review of the Current State-of-the-Art.

Epilepsy affects ∼ 1% of the global population, and 33% of patients are nonresponsive to medication and must seek alternative treatment options. Alternative options such as surgery and ablation exist but are not appropriate treatment plans for some patients. Neurostimulation methods such as vagal nerve stimulation, responsive neural stimulation, and deep brain stimulation (DBS) are viable alternatives for medically refractory patients. DBS stimulation has been used in the treatment of Parkinson's disease, dystonia, and pain management. For the treatment of epilepsy, DBS has been found to be an effective treatment plan, with promising results of reduced seizure frequency and intensity. In this review, we discuss DBS surgery and equipment, mechanisms of DBS for epilepsy, and efficacy, technological specifications, and suggestions for future research. We also review a historical summary of experiments involving DBS for epilepsy. Our literature review suggests that further studies are warranted for medically refractory epilepsy using DBS.

Electrostimulation for Refractory Epilepsy: A Review.

Epilepsy is a neurological disorder that has been diagnosed in approximately 1% of the world's population. In North America alone, more than 3 million individuals suffer from epilepsy. Antiepileptic drugs are not fully effective in some patients, and most drugs have adverse side effects. Recently, several stimulation techniques (responsive neural, vagal nerve, transcranial magnetic, and deep brain) have been used as adjunct therapies to treat medically refractive seizures. Since its Food and Drug Administration approval in 2013, responsive neural stimulation (RNS), a closed-loop electrical stimulation system, has emerged as a potential therapeutic alternative to treat patients with epilepsy (PWE). RNS consists of a cranially implantable neurostimulator that sends electrical pulses using depth electrodes to epileptic foci/focus after the device senses irregular electrical activity, thus avoiding the onset of a seizure. In a long-term study that lasted 7 yr and involved more than 245 patients using RNS, results showed that16% of patients were seizure free, 60% had 50% or greater seizure reduction, and 84% had some improvement. Quality of life improved in 44% of the patients by the end of the second year. There is a need for more, larger, well-designed, randomized, controlled trials to validate and optimize efficacy and safety of invasive intracranial neurostimulation treatments in PWE. This article highlights the effects of treating patients with medically refractive seizures using RNS.

Air Pollution and Cardiovascular Disease: A Review.

Air pollution is comprised of different compounds and particulate matter (PM) of sizes 2.5 and 10 µm, with the former size posing the greatest danger to humans. Evidence suggests that the global rise in air pollution levels during the past century is correlated with the increased incidence of diseases of the cardiovascular system. On a global scale, 7 million individuals died as a result of the effects of air pollution in 2012. Air pollution leads to tremendous amounts of financial burden (in 2010, $16 trillion in the US and Europe) on the health-care system. The severity of effects experienced by varying populations due to air pollution can differ due to locale, length of exposure, weather conditions, residential proximity to major highways or factories, and soil aridity. Pollutants affect the heart, blood vessels, and blood at a molecular level through proinflammatory or oxidative stress response, autonomic nervous system imbalance, and the direct permeation of harmful compounds into the tissue. The dysfunction of cells and biological processes of the cardiovascular system due to PM leads to an increased prevalence of cardiovascular diseases (CVDs) such as atherosclerosis, hypertension, myocardial infarction, thrombosis, and restricted valve motion. Studies in countries such as China have shown an increase of 0.25% in ischemic heart disease (IHD) mortality and a 0.27% increase in IHD morbidity due to a 10 µg/m3 increase in PM. In a study conducted in the US, PM2.5 concentrations ranged from 9.2-22.6 µg/m3, and every 5-µg/m3 increase in PM2.5 caused coronary calcification to increase by 4.1 Agatston units/yr. Studies on traffic-related air pollution found that nonhypertensive participants residing within 100 m of major roadways experienced an increase in systolic (0.35 mmHg) and diastolic (0.22 mmHg) blood pressure as a result of increases in traffic. The progression of CVD due to pollution has been found to fluctuate within individuals based on age, gender, location of exercise, smoking, pregnancy, diabetes, preexisting cardiovascular or pulmonary diseases, and other factors. Considering the number of individuals affected by pollution on a daily basis and the burden that this places on society through the health-care system, immediate preventive measures are needed to address these problems. Increased knowledge about the widespread effects of pollution on human physiological systems should aid in remediating the problem across the globe. Biomedical engineers can have a great positive impact in developing better instrumentation to measure discrete pollutants and characterizing their harmful effects on physiological systems.

Air Pollution's Effects on the Human Respiratory System.

The World Health Organization defines air pollution as "any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere." The most common pollutants include particulate matter, carbon monoxide, ozone, nitrogen oxide, and sulfur dioxide. The two types of air pollution, indoor and ambient, both contribute to a host of cardiac and respiratory illnesses. Exposure to excess levels of air pollution is significantly associated with a variety of acute and chronic respiratory illnesses, such as chronic obstructive pulmonary disease, asthma, respiratory allergies, and lung cancer. The effects of air pollution disproportionately impact the extremes of the age distribution, perhaps due to altered immune responses. Athletes and those who exercise outdoors are at greater risk for the respiratory effects of air pollution. This article discusses the epidemiology, types of respiratory diseases, and mechanisms involved in exposure to excess levels of air pollution. Biomedical engineering can contribute to the identification of air pollutants through the design of novel instrumentation using materials based on nanotechnology. Mathematical models can also be developed to characterize the physiological effects of air pollution.

Deep Learning in Gastrointestinal Endoscopy.

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Heart rate variability biofeedback as a behavioral neurocardiac intervention to enhance vagal heart rate control.

BACKGROUND: Patients with coronary heart disease (CHD) who experience depressed mood or psychological stress exhibit decreased vagal control of heart rate (HR), as assessed by spectral analysis of HR variability (HRV). Myocardial infarction and sudden cardiac death are independently associated with depression and stress, as well as impaired vagal HR control. This study examined whether a behavioral neurocardiac intervention to reduce stress or depression can augment cardiovagal modulation in CHD patients. We hypothesized that (1) cognitive-behavioral training with HRV biofeedback would augment vagal recovery from acute stress, and (2) vagal regulation of HR would be inversely associated with stress and depression after treatment. METHODS: This randomized controlled trial enrolled 46 CHD patients from 3 clinics of CHD risk reduction in Toronto and Vancouver, Canada. Subjects were randomized to five 1.5-hour sessions of HRV biofeedback or an active control condition. Outcome was assessed by absolute and normalized high-frequency spectral components (0.15-0.50 Hz) of HRV, and by the Perceived Stress Scale and Centre for Epidemiologic Studies in Depression scale. RESULTS: Both groups reduced symptoms on the Perceived Stress Scale (P = .001) and Centre for Epidemiologic Studies in Depression scale (P = .004). Hierarchical linear regression determined that improved psychological adjustment was significantly associated with the high-frequency index of vagal HR modulation only in the HRV biofeedback group. Adjusted R 2 was as follows: HRV biofeedback group, 0.86 for stress (P = .02) and 0.81 for depression (P = .03); versus the active control group, 0.04 (P = .57) and 0.13 (P = .95), respectively. CONCLUSION: A novel behavioral neurocardiac intervention, HRV biofeedback, can augment vagal HR regulation while facilitating psychological adjustment to CHD.

Optimal electrical stimulation modality for cortical esophageal evoked potentials: transmural or intraesophageal?

Esophageal electrical stimulation using short and a relatively small number of (200 micros, 0.2 Hz, n = 25) electrical pulses generates a characteristic and well defined cortical evoked potential response (EP). There are two methods of stimulation: either through intraesophageal electrodes or with transmural electrodes. The objective of this paper is to compare EP response, sensations and heart rate variability power spectra elicited by both stimulation modalities in healthy volunteers. Our results suggest that transmural stimulation is more accurately perceived and at lower intensities, produces more reproducible peaks of higher amplitude than during intraesophageal stimulation. During either mode of esophageal stimulation, power within the high-frequency component of the heart rate variability power spectrum is enhanced.

Review of Texture Quantification of CT Images for Classification of Lung Diseases.

Computer-based identification of abnormal regions and classification of diseases using CT images of the lung has been a goal of many investigators. In this paper, we review research that has used texture analysis along with segmentation and fractal analysis. First, a review of texture methods is performed. Recent research on quantitative analysis of the lung using texture methods is categorized into six groups of computational methods: structural, statistical, model based, transform domain, texture-segmentation, and texture-fractal analysis. Finally, the applications of texture-based methods combined with either segmentation algorithms or fractal analysis is evaluated on lung CT images from patients with diseases such as emphysema, COPD, and cancer. We also discuss applications of artificial neural networks, support vector machine, k-nearest, and Bayesian methods to classify normal and diseased segments of CT images of the lung. A combination of these texture methods followed by classifiers could lead to efficient and accurate diagnosis of pulmonary diseases such as pulmonary fibrosis, emphysema, and cancer.

Isometric training lowers resting blood pressure and modulates autonomic control.

PURPOSE: This study examined the effects of isometric handgrip training on resting arterial blood pressure, heart rate variability, and blood pressure variability in older adults with hypertension. METHODS: Nine subjects performed four 2-min isometric handgrip contractions at 30% of the maximum voluntary contraction force, 3 d.wk(-1) for 10 wk, and eight subjects served as controls. RESULTS: After training, there was a significant reduction in resting systolic pressure and mean arterial pressure. In addition, power spectral analysis of heart rate variability demonstrated that the low frequency: high frequency area ratio tended to decrease. CONCLUSIONS: It is concluded that isometric training at a moderate intensity elicits a hypotensive response and a simultaneous increase in vagal modulation in older adults with hypertension.