A semi-automated method for analyzing cardiac quiescence of anatomical cardiac features from two-dimensional echocardiographic cine data is presented. The method utilizes both active contour and optical flow techniques for feature identification and tracking. A curvature-based potential surface was used in the active contour calculations to attract the contour to regions of inflection on the image surface rather than the standard gradient-based surface that attracts the contour to strong edges. After identifying the feature in each frame, the frame-to-frame correlation matrix of the feature was calculated with correlation values corresponding to how well the feature matched between frames. Therefore prolonged regions of high correlation correspond to periods of cardiac quiescence. The location and duration of these periods were automatically identified from the correlation matrix by finding the largest region around each time index with a mean correlation above a specified threshold. In parallel, the position of the feature was calculated for each frame by finding the centroid of the pixel locations inside the contour. From this trajectory, the magnitude of the two-dimensional velocity was calculated. These methods were used to analyze the quiescence of the interventricular septum from an apical four-chamber echocardiogram performed on a human subject. Correlation-derived quiescent phases were observed to coincide with periods of the cardiac cycle with minimal velocity magnitude.
Wearable technology may provide an integral part of the solution for providing health care to a growing world population that will be strained by a ballooning aging population. By providing a means to conduct telemedicine-the monitoring, recording, and transmission of physiological signals from outside of the hospital-wearable technology solutions could ease the burden on health-care personnel and use hospital space for more emergent or responsive care. In addition, employing wearable technology in professions where workers are exposed to dangers or hazards could help save their lives and protect health-care personnel.
This paper reviewed the transcranial magnetic stimulation (TMS) applications for brain-behavior relations. Along with rapid-rate TMS, coils designed for focal stimulation and image-guided targeting of stimulation to desired cortical structures, neuronal processes can now be disrupted even in relatively well-defined cortical areas. Delivering two sequential pulses to the primary motor cortex(MI) allowed the exploration of inhibition and facilitation within the motor pathways. Function brain imaging was combined with TMS for access of extent and loci of local and remote TMS-induced brain effects. Reflecting changes in cerebral blood flow and oxygenation, 15O-H 2 O or 18 F-FDG positron emission tomography, 99 mTc ethylcysteinate dimer single photon emission tomography, blood-oxygenation-level dependent magnetic resonance imaging (MRI), and near infrared spectroscopy showed bilateral cortical activity as well as activation of subcortical structures and the cerebellum. The introduction of TMS-compatible EEG allowed one to measure the instant and direct neuronal effects of TMS (Figure 6); bilateral activation patterns, similar to functional imaging, were exhibited in source images derived from the EEG. Intriguing examples of the use of TMS/EEG technique in studying corticocortical connectivity are already available in the literature. TMS shows a great promise for future clinical applications. Presently, the key topics in TMS research include altered cortical excitability in neurological diseases, functional relevance of cortical areas in cognitive task performance, and treatment of psychiatric diseases. To date, however, there are not enough data to establish TMS studies as part of clinical diagnostics or therapy in any neurological or psychiatric disease. Recently, patterned types of stimulation have been introduced, each still under scrutiny, to define its efficacy in research and clinical settings as well as its potential hazards. The last decade has seen a rapid increase in the applications of TMS to study cognition, brain-behavior relations, and the pathophysiology of various neurologic and psychiatric disorders. In addition, evidence has accumulated that demonstrated that TMS provides a valuable tool for interventional neurophysiology applications, modulating brain activity in a specific, distributed, corticosubcortical network. Finally, recent evidences have shown that multiple muscle recordings allow to probe dynamic properties of the multiple representations at the cortical levels to orchestrate the best movement performance(Figure 7).
Natural language processing (NLP) is a subfield of computational sciences addressing the operation and management of texts as inputs or outputs of computational devices. The reviewed book is an excellent resource for researchers or graduate students interested in NLP, artificial intelligence, and linguistics, as well as those interested in the most recent advances in information management and retrieval in a health-care setting.
This paper was focused on the human energy expenditure and metabolism. Since EE ends up as heat, direct or indirect physiological calorimetry was used to measure heat lost. For indirect calorimetry, metabolic carts, portable calorimeters and whole-room indirect calorimeters were used. Physical activity and heart rate monitoring were conducted.
The various components of the artificial pancreas puzzle are being put into place. Features such as communication, control, modeling, and learning are being realized presently. Steps have been set in motion to carry the conceptual design through simulation to clinical implementation. The challenging pieces still to be addressed include stress and exercise; as integral parts of the ultimate goal, effort has begun to shift toward overcoming the remaining hurdles to the full artificial pancreas. The artificial pancreas is close to becoming a reality, driven by technology, and the expectation that lives will be improved.
The purpose of this article was to address three questions: What were the electronic data-capturing (EDC) technologies employed in a typical industry-sponsored clinical study? How is the developed system meeting the clinical research need? What would we want more from this EDC technology? This article is prepared from industry perspectives to present and analyze the advantages, benefits, and challenges in applying EDC technologies to address industry's clinical trial operational needs based on a systematic overview.
Nerve conduction studies (NCSs) have played an important role in the evaluation of neuromuscular disease for the past 50 years. When patients present with complaints of pain, numbness, tingling, or weakness, NCS is often one of the earliest tests obtained by physicians, because it enables the quantitative assessment of peripheral nerve and muscle function and, therefore, aid the physician in identifying the physiological source of the patient's symptoms. NCSs involve the delivery of electric stimuli to peripheral nerves at accessible locations on the human body and the recording of electrophysiological responses. This article reviews how NCS is traditionally performed. This paper also examines technical challenges associated with each step of performing an NCS and describes how engineering solutions could be realized to meet these challenges. The engineering goals were several: improvement in NCS workflow, use of prefabricated electrode arrays to standardize NCS technique and reduce the errors associated with electrode placement, and improvement of the overall accuracy and reliability of NCS.
Multiple studies suggest that the level of patient care may decline in the future because of a larger aging population and medical staff shortages. Wireless sensing systems that automate some of the patient monitoring tasks can potentially improve the efficiency of patient workflows, but their efficacy in clinical settings is an open question. This article examines the potential of wireless sensor network (WSN) technologies to improve the efficiency of the patient-monitoring process in clinical environments. MEDiSN, a WSN designed to continuously monitor the vital signs of ambulatory patients, is designed. The usefulness of MEDiSN is validated with test bed experiments and results from a pilot study performed at the Emergency Department, Johns Hopkins Hospital. Promising results indicate that MEDiSN can tolerate high degrees of human mobility, is well received by patients and staff members, and performs well in real clinical environments.
This book is quite extensive, covering basic bioinformatics concepts, biological data modeling techniques, and real-world integration/system interoperability challenges. It is an excellent reference for bioinformatics graduate students, researchers, clinical scientists, clinical data architects, and drug discovery professionals interested in managing postgenome biological data and systems.
New Year greetings! As we are entering into 2010, I would like to report our Society's activities in each portfolio. In September 2009, IEEE Engineering in Medicine and Biology Society (EMBS) Administrative Committee (AdCom) elected new members of the Executive Committee (ExCom). Zhi-Pei Liang was elected as president elect (2010), Nigel Lovell as vice president (VP) conferences (2010--2011), and Gudrun Zahlmann as VP members and Student Activities (2010--2011).
Starting in 2010, the IEEE Engineering in Medicine and Biology Society (EMBS) is launching a new series of forums addressing the grand challenges in biomedical engineering. The forum series will review the significant progress we have made in the past decade and identify the grand challenges facing the scientific community in a specific discipline within the biomedical engineering field in the next ten years.
In this review, the current state of mathematical modeling of human metabolism was outlined together with the regulation of body weight and body composition. The following topics were discussed: energy balance models of body-weight change; mass-energy conversion factor and its relationship to body composition change; modelling steady-state changes of body weight; weight-loss kinetic modeling; metabolic fuel selection modelling beyond energy-balance models; fuel selection physiological mechanisms modelling; and, applications to wasting conditions such as cancer cachexia and future directions.
Writing about obesity research is a challenging task. While the rising obesity epidemic drastically raised public awareness of the problem, the causes behind the epidemic are still poorly understood. The etiology of obesity is a subject of ongoing scientific debate with widely varying views and strong opinions. Is it mostly genetic or environmental in nature? Is obesity caused by changes in our diet or changes in lifestyle and physical activity or both? Modern research literature quite often offers conflicting findings. Publications in popular media like the one in Time magazine add to the controversy by making quick and strongly worded summaries of academic research. Although the root causes of obesity remains a topic of active research, this review concentrates on the fundamental components of weight regulation in humans and their relative contribution to the energy equation. A better understanding of the energetics of obesity may provide some insight into the etiology of the obesity epidemic. The energetics of obesity also showcases an engineering challenge: development of techniques to accurately measure individual components of the energy equation.
As the volume of data that is electronically available promliferates, the health-care industry is identifying better ways to use this data for patient care. Ideally, these data are collected in real time, can support point-of-care clinical decisions, and, by providing instantaneous quality metrics, can create the opportunities to improve clinical practice as the patient is being cared for. The business-world technology supporting these activities is referred to as business intelligence, which offers competitive advantage, increased quality, and operational efficiencies. The health-care industry is plagued by many challenges that have made it a latecomer to business intelligence and data-mining technology, including delayed adoption of electronic medical records, poor integration between information systems, a lack of uniform technical standards, poor interoperability between complex devices, and the mandate to rigorously protect patient privacy. Efforts at developing a health care equivalent of business intelligence (which we will refer to as clinical intelligence) remains in its infancy. Until basic technology infrastructure and mature clinical applications are developed and implemented throughout the health-care system, data aggregation and interpretation cannot effectively progress. The need for this approach in health care is undisputed. As regional and national health information networks emerge, we need to develop cost-effective systems that reduce time and effort spent documenting health-care data while increasing the application of knowledge derived from that data.
This book is an overview of biomaterials design, specifically as it relates to the interaction of biomaterials surfaces and the biological system in which it is implanted. It is meant to provide an overview for engineers, material scientists, biologists, and others interested in problems involving biomaterials and biocompatibility.
This paper investigates the use of clustering technique to characterize the providers of maintenance services in a health-care institution according to their performance. A characterization of the inventory of equipment from seven pilot areas was carried out first (including 264 medical devices). The characterization study concluded that the inventory on a whole is old [exploitation time (ET)/useful life (UL) average is 0.78] and has high maintenance service costs relative to the original cost of acquisition (service cost /acquisition cost average 8.61%). A monitoring of the performance of maintenance service providers was then conducted. The variables monitored were response time (RT), service time (ST), availability, and turnaround time (TAT). Finally, the study grouped maintenance service providers into clusters according to performance. The study grouped maintenance service providers into the following clusters. Cluster 0: Identified with the best performance, the lowest values of TAT, RT, and ST, with an average TAT value of 1.46 days; Clusters 1 and 2: Identified with the poorest performance, highest values of TAT, RT, and ST, and an average TAT value of 9.79 days; and Cluster 3: Identified by medium-quality performance, intermediate values of TAT, RT, and ST, and an average TAT value of 2.56 days.
The purpose of this study was to serve as a reference for further research improvements and to address the current level of maturity of each methodology from its maximum capabilities. This paper discussed the following: automatic detection of intestinal juices in wireless capsule video endoscopy; neural networks-based approach; model of deformable rings for aiding the WCE video interpretation and reporting; digestive organ automatic image classification; WCE blood detection using expectation maximization clustering; discriminate tissues color distributions; color- and texture-based GI tissue discrimination; topographic segmentation and transit time estimation for endoscopic capsule exams; automated tissue classification; colonoscopic diagnosis using online learning and differential evolution; computer-aided tumor detection using color wavelet features; versatile coLD detection system for colorectal lesions; images sequences; blood-based abnormalities detection; and other related topics.
