Although magnetic resonance spectroscopy (MRS) methods of 1.5Tesla (T) and 3T have been widely applied during the last decade for noninvasive diagnostic purposes, only a few studies have been reported on the value of the information extracted in brain cancer discrimination. The purpose of this study is threefold. First, to show that the diagnostic value of the information extracted from two different MRS scanners of 1.5T and 3T is significantly influenced in terms of brain gliomas discrimination. Second, to statistically evaluate the discriminative potential of publicly known metabolic ratio markers, obtained from these two types of scanners in classifying low-, intermediate-, and high-grade gliomas. Finally, to examine the diagnostic value of new metabolic ratios in the discrimination of complex glioma cases where the diagnosis is both challenging and critical. Our analysis has shown that although the information extracted from 3T MRS scanner is expected to provide better brain gliomas discrimination; some factors like the features selected, the pulse-sequence parameters, and the spectroscopic data acquisition methods can influence the discrimination efficiency. Finally, it is shown that apart from the bibliographical known, new metabolic ratio features such as N-acetyl aspartate/ S, Choline/ S, Creatine/ S , and myo-Inositol/ S play significant role in gliomas grade discrimination.
Medical devices are essential to the practice of modern healthcare services. Their benefits will increase if clinical software applications can seamlessly acquire the medical device data. The need to represent medical device observations in a format that can be consumable by clinical applications has already been recognized by the industry. Yet, the solutions proposed involve bilateral mappings from the ISO/IEEE 11073 Domain Information Model (DIM) to specific message or document standards. Considering that there are many different types of clinical applications such as the electronic health record and the personal health record systems, the clinical workflows, and the clinical decision support systems each conforming to different standard interfaces, detailing a mapping mechanism for every one of them introduces significant work and, thus, limits the potential health benefits of medical devices. In this paper, to facilitate the interoperability of clinical applications and the medical device data, we use the ISO/IEEE 11073 DIM to derive an HL7 v3 Refined Message Information Model (RMIM) of the medical device domain from the HL7 v3 Reference Information Mode (RIM). This makes it possible to trace the medical device data back to a standard common denominator, that is, HL7 v3 RIM from which all the other medical domains under HL7 v3 are derived. Hence, once the medical device data are obtained in the RMIM format, it can easily be transformed into HL7-based standard interfaces through XML transformations because these interfaces all have their building blocks from the same RIM. To demonstrate this, we provide the mappings from the developed RMIM to some of the widely used HL7 v3-based standard interfaces.
This paper presents the design and implementation of an architecture based on the integration of simple network management protocol version 3 (SNMPv3) and the standard ISO/IEEE 11073 (X73) to manage technical information in home-based telemonitoring scenarios. This architecture includes the development of an SNMPv3-proxyX73 agent which comprises a management information base (MIB) module adapted to X73. In the proposed scenario, medical devices (MDs) send information to a concentrator device [designated as compute engine (CE)] using the X73 standard. This information together with extra information collected in the CE is stored in the developed MIB. Finally, the information collected is available for remote access via SNMP connection. Moreover, alarms and events can be configured by an external manager in order to provide warnings of irregularities in the MDs' technical performance evaluation. This proposed SNMPv3 agent provides a solution to integrate and unify technical device management in home-based telemonitoring scenarios fully adapted to X73.
Wireless body sensor network (WBSN), a key building block for m-Health, demands extremely stringent resource constraints and thus lightweight security methods are preferred. To minimize resource consumption, utilizing information already available to a WBSN, particularly common to different sensor nodes of a WBSN, for security purposes becomes an attractive solution. In this paper, we tested the randomness and distinctiveness of the 128-bit biometric binary sequences (BSs) generated from interpulse intervals (IPIs) of 20 healthy subjects as well as 30 patients suffered from myocardial infarction and 34 subjects with other cardiovascular diseases. The encoding time of a biometric BS on a WBSN node is on average 23 ms and memory occupation is 204 bytes for any given IPI sequence. The results from five U.S. National Institute of Standards and Technology statistical tests suggest that random biometric BSs can be generated from both healthy subjects and cardiovascular patients and can potentially be used as authentication identifiers for securing WBSNs. Ultimately, it is preferred that these biometric BSs can be used as encryption keys such that key distribution over the WBSN can be avoided.
In this study, we applied an iterative independent component analysis (ICA) method for the separation of cardiac tissue components (myocardium, right, and left ventricle) from dynamic positron emission tomography (PET) images. Previous phantom and animal studies have shown that ICA separation extracts the cardiac structures accurately. Our goal in this study was to investigate the methodology with human studies. The ICA separated cardiac structures were used to calculate the myocardial perfusion in two different cases: 1) the regions of interest were drawn manually on the ICA separated component images and 2) the volumes of interest (VOI) were automatically segmented from the component images. For the whole myocardium, the perfusion values of 25 rest and six drug-induced stress studies obtained with these methods were compared to the values from the manually drawn regions of interest on differential images. The separation of the rest and stress studies using ICA-based methods was successful in all cases. The visualization of the cardiac structures from H (2) (15) O PET studies was improved with the ICA separation. Also, the automatic segmentation of the VOI seemed to be feasible.
This paper describes the design of a telemedicine system based on next-generation wireless local area networks (WLANs) operating at 17 GHz. Seventeen gigahertz is proposed for next-generation WLAN services offering numerous advantages over traditional IEEE 802.11 networks that operate in the range of 2.4-5 GHz. Orthogonal polarization is often used to increase spectrum efficiency by utilizing signal paths of horizontal and vertical polarization. Radio waves exceeding 10 GHz are particularly vulnerable to signal degradation under the influence of rain which causes an effective reduction in isolation between polarized signal paths. This paper investigates the influence of heavy rain in a tropical region on wide-band microwave signals at 17 GHz using two links provided by a fixed broad-band wireless access system for two-way data exchange between paramedics attending an accident scene and the hospital via microwave equipment installed in the ambulance. We also study the effects of cross polarization and phase rotation due to persistent heavy rainfall in tropical regions.
This paper aims to develop an infobutton to automatically retrieve published papers corresponding to a topic-specific online clinical discussion. The knowledge linkages infobutton is designed to supplement online clinical conversations with pertinent medical literature from Pubmed. The project involves three distinct steps: 1) Clinical messages around a specific problem are grouped together into a thread. 2) These threads are processed using Metamap to link the conversations to keywords from the MeSH lexicon. 3) These keywords are used in a novel search strategy to retrieve a set of papers from Pubmed, which are then returned to the user. A pilot study using the messages from 2007 and 2008, was conducted to compare the knowledge linkage search strategy to a vector space model and extended Boolean model. The knowledge linkage model proved to be significantly better in terms of precision ( p = 0.013 and 0.003, respectively) and recall ( p = 0.351 and 0.013). Pertinent papers were returned to over 55% of the threads. This approach has demonstrated how clinicians can supplement their peer communications with evidence based research. Future work should focus on how to improve the threading and keyword-mapping strategies.
This paper details the implementation and operational performance of a minimum-power 2.45-GHz pulse receiver and a companion on-off keyed transmitter for use in a semi-active, duplex RF biomedical transponder. A 50-ohm microstrip stub-matched zero-bias diode detector forms the heart of a body-worn receiver that has a (CMOS baseband amplifier consuming 20 microA from +3 V and achieves a tangential sensitivity of -53 dBm. The base transmitter generates 0.5 W of peak RF output power into 50 ohms. Both linear and right-hand circularly polarized Tx-Rx antenna sets were employed in system reliability trials carried out in a hospital Coronary Care Unit. For transmitting antenna heights between 0.3 and 2.2 m above floor level, transponder interrogations were 95% reliable within the 67-m2 area of the ward, falling to an average of 46% in the surrounding rooms and corridors. Overall, the circular antenna set gave the higher reliability and lower propagation power decay index.
Telemedicine (health-care delivery where physicians examine distant patients using telecommunications technologies) has been heralded as one of several possible solutions to some of the medical dilemmas that face many developing countries. In this study, we examine the current state of telemedicine in a developing country, India. Telemedicine has brought a plethora of benefits to the populace of India, especially those living in rural and remote areas (constituting about 70% of India's population). We discuss three Indian telemedicine implementation cases, consolidate lessons learned from the cases, and culminate with potential researchable critical success factors that account for the growth and modest successes of telemedicine in India.
The January 2011 Special Issue of IEEE Transactions on Information Technology in Biomedicine includes selected papers from Information Technology in Biomedicine (ITAB 2009), held in the month of October in Cyprus. The overall objective of ITAB 2009 was to cover the state of the art in Information Technology Applications in Biomedicine, under the theme 'Citizen centered e-health systems in a global healthcare environment'. The Issue contains paper by Karagiannis and colleagues that investigates the performance of empirical mode decomposition (EMD) on ECGs. The paper by Stylianides introduces a novel, open source middleware framework for communication with medical devices and an application using the middleware named intensive care window (ICW). Cunningham and colleagues propose a home-based tool designed to monitor and assess peripheral motor symptoms. Loizou and colleagues introduce the use of multiscale AM FM texture analysis of multiple sclerosis (MS) using MR images from the brain.
In this study, we describe an effective video communication framework for the wireless transmission of H.264/AVC medical ultrasound video over mobile WiMAX networks. Medical ultrasound video is encoded using diagnostically-driven, error resilient encoding, where quantization levels are varied as a function of the diagnostic significance of each image region. We demonstrate how our proposed system allows for the transmission of high-resolution clinical video that is encoded at the clinical acquisition resolution and can then be decoded with low-delay. To validate performance, we perform OPNET simulations of mobile WiMAX Medium Access Control (MAC) and Physical (PHY) layers characteristics that include service prioritization classes, different modulation and coding schemes, fading channels conditions, and mobility. We encode the medical ultrasound videos at the 4CIF (704×576) resolution that can accommodate clinical acquisition that is typically performed at lower resolutions. Video quality assessment is based on both clinical (subjective) and objective evaluations.
We propose a unifying framework for efficient encoding, transmission, and quality assessment of atherosclerotic plaque ultrasound video. The approach is based on a spatially varying encoding scheme, where video-slice quantization parameters are varied as a function of diagnostic significance. Video slices are automatically set based on a segmentation algorithm. They are then encoded using a modified version of H.264/AVC flexible macroblock ordering (FMO) technique that allows variable quality slice encoding and redundant slices (RSs) for resilience over error-prone transmission channels. We evaluate our scheme on a representative collection of ten ultrasound videos of the carotid artery for packet loss rates up to 30%. Extensive simulations incorporating three FMO encoding methods, different quantization parameters, and different packet loss scenarios are investigated. Quality assessment is based on a new clinical rating system that provides independent evaluations of the different parts of the video (subjective). We also use objective video-quality assessment metrics and estimate their correlation to the clinical quality assessment of plaque type. We find that some objective quality assessment measures computed over the plaque video slices gave very good correlations to mean opinion scores (MOSs). Here, MOSs were computed using two medical experts. Experimental results show that the proposed method achieves enhanced performance in noisy environments, while at the same time achieving significant bandwidth demands reductions, providing transmission over 3G (and beyond) wireless networks.
Continuous care models for chronic diseases pose several technology-oriented challenges for home-based care, where assistance services rely on a close collaboration among different stakeholders, such as health operators, patient relatives, and social community members. This paper describes an ontology-based context model and a related context management system providing a configurable and extensible service-oriented framework to ease the development of applications for monitoring and handling patient chronic conditions. The system has been developed in a prototypal version, and integrated with a service platform for supporting operators of home-based care networks in cooperating and sharing patient-related information and coordinating mutual interventions for handling critical and alarm situations. Finally, we discuss experimentation results and possible further research directions.
Visualized freehand 3-D ultrasound reconstruction offers to image incremental reconstruction during acquisition and guide users to scan interactively for high-quality volumes. We originally used the graphics processing unit (GPU) to develop a visualized reconstruction algorithm that achieves real-time level. Each newly acquired image was transferred to the memory of the GPU and inserted into the reconstruction volume on the GPU. The partially reconstructed volume was then rendered using GPU-based incremental ray casting. After visualized reconstruction, hole-filling was performed on the GPU to fill remaining empty voxels in the reconstruction volume. We examine the real-time nature of the algorithm using in vitro and in vivo datasets. The algorithm can image incremental reconstruction at speed of 26-58 frames/s and complete 3-D imaging in the acquisition time for the conventional freehand 3-D ultrasound.
In order to understand the brain, we need to first understand the morphology of neurons. In the neurobiology community, there have been recent pushes to analyze both neuron connectivity and the influence of structure on function. Currently, a technical roadblock that stands in the way of these studies is the inability to automatically trace neuronal structure from microscopy. On the image processing side, proposed tracing algorithms face difficulties in low contrast, indistinct boundaries, clutter, and complex branching structure. To tackle these difficulties, we develop Tree2Tree, a robust automatic neuron segmentation and morphology generation algorithm. Tree2Tree uses a local medial tree generation strategy in combination with a global tree linking to build a maximum likelihood global tree. Recasting the neuron tracing problem in a graph-theoretic context enables Tree2Tree to estimate bifurcations naturally, which is currently a challenge for current neuron tracing algorithms. Tests on cluttered confocal microscopy images of Drosophila neurons give results that correspond to ground truth within a margin of ±2.75% normalized mean absolute error.
This paper introduces a cost-effective portable teletrauma system that assists health-care centers in providing prehospital trauma care. Simultaneous transmission of a patient's video, medical images, and electrocardiogram signals, which is required throughout the prehospital procedure, is demonstrated over commercially available 3G wireless cellular data service. Moreover, the physician can remotely control the information sent from the patient side. Such a technology will allow a trauma specialist to be virtually present at the remote location and participate in prehospital care, which improves the quality of trauma care and can potentially reduce mortality and morbidity. To alleviate the limited and fluctuant bandwidth barriers of the wireless cellular link, the system adapts to network conditions through media transformations, data prioritization, and application-level congestion control methods. Experimental evaluation of the system prototype over real network conditions, transmitting different media types between the trauma patient and hospital unit, is encouraging. The teletrauma system reported in this paper is the first of its kind and it provides a basis for future enhancements.
In the last decade, the seminal term and concept of "m-health" were first defined and introduced in this transactions as "mobile computing, medical sensor, and communications technologies for healthcare." Since that special section, the m-health concept has become one of the key technological domains that reflected the key advances in remote healthcare and e-health systems. The m-health is currently bringing together major academic research and industry disciplines worldwide to achieve innovative solutions in the areas of healthcare delivery and technology sectors. From the wireless communications perspective, the current decade is expected to bring the introduction of new wireless standards and network systems with true mobile broadband and fast internet access healthcare services. These will be developed around what is currently called the fourth-generation (4G) mobile communication systems. In this editorial paper, we will introduce the new and novel concept of 4G health that represents the long-term evolution of m-health since the introduction of the concept in 2004. The special section also presents a snapshot of the recent advances in these areas and addresses some of the challenges and future implementation issues from the evolved m-health perspective. It will also present some of the concepts that can go beyond the traditional "m-health ecosystem" of the existing systems. The contributions presented in this special section represent some of these developments and illustrate the multidisciplinary nature of this important and emerging healthcare delivery concept.
In wireless personal area networks, such as wireless body-area sensor networks, stations or devices have different bandwidth requirements and, thus, create heterogeneous traffics. For such networks, the IEEE 802.15.4 medium access control (MAC) can be used in the beacon-enabled mode, which supports guaranteed time slot (GTS) allocation for time-critical data transmissions. This paper presents a general discrete-time Markov chain model for the IEEE 802.15.4-based networks taking into account the slotted carrier sense multiple access with collision avoidance and GTS transmission phenomena together in the heterogeneous traffic scenario and under nonsaturated condition. For this purpose, the standard GTS allocation scheme is modified. For each non-identical device, the Markov model is solved and the average service time and the service utilization factor are analyzed in the non-saturated mode. The analysis is validated by simulations using network simulator version 2.33. Also, the model is enhanced with a wireless propagation model and the performance of the MAC is evaluated in a wheelchair body-area sensor network scenario.
Identifying abdominal organs is one of the essential steps in visualizing organ structure to assist in teaching, clinical training, diagnosis, and medical image retrieval. However, due to partial volume effects, gray-level similarities of adjacent organs, contrast media affect, and the relatively high variations of organ position and shape, automatically identifying abdominal organs has always been a high challenging task. To conquer these difficulties, this paper proposes combining a multimodule contextual neural network and spatial fuzzy rules and fuzzy descriptors for automatically identifying abdominal organs from a series of CT image slices. The multimodule contextual neural network segments each image slice through a divide-and-conquer concept, embedded within multiple neural network modules, where the results obtained from each module are forwarded to other modules for integration, in which contextual constraints are enforced. With this approach, the difficulties arising from partial volume effects, gray-level similarities of adjacent organs, and contrast media affect can be reduced to the extreme. To address the issue of high variations in organ position and shape, spatial fuzzy rules and fuzzy descriptors are adopted, along with a contour modification scheme implementing consecutive organ region overlap constraints. This approach has been tested on 40 sets of abdominal CT images, where each set consists of about 40 image slices. We have found that 99% of the organ regions in the test images are correctly identified as its belonging organs, implying the high promise of the proposed method.
Organ segmentation is an important step in various medical image applications. In this paper, a presegmented atlas is incorporated into the fuzzy connectedness (FC) framework for automatic segmentation of abdominal organs. First, the atlas is registered onto the subject to provide an initial segmentation. Then, a novel method is applied to estimate the necessary FC parameters such as organ intensity features, seeds, and optimal FC threshold automatically and subject adaptively. In order to overcome the intensity overlapping between the neighboring organs, a shape modification approach based on Euclidean distance and watershed segmentation is used. This atlas-based segmentation method has been tested on some abdominal CT and MRI images from Chinese patients. Experimental results indicate the validity of this segmentation method for various image modalities.
Geometrical changes of blood vessels, called aneurysm, occur often in humans with possible catastrophic outcome. Then, the blood flow is enormously affected, as well as the blood hemodynamic interaction forces acting on the arterial wall. These forces are the cause of the wall rupture. A mechanical quantity characteristic for the blood-wall interaction is the wall shear stress, which also has direct physiological effects on the endothelial cell behavior. Therefore, it is very important to have an insight into the blood flow and shear stress distribution when an aneurysm is developed in order to help correlating the mechanical conditions with the pathogenesis of pathological changes on the blood vessels. This insight can further help in improving the prevention of cardiovascular diseases evolution. Computational fluid dynamics (CFD) has been used in general as a tool to generate results for the mechanical conditions within blood vessels with and without aneurysms. However, aneurysms are very patient specific and reliable results from CFD analyses can be obtained by a cumbersome and time-consuming process of the computational model generation followed by huge computations. In order to make the CFD analyses efficient and suitable for future everyday clinical practice, we have here employed data mining (DM) techniques. The focus was to combine the CFD and DM methods for the estimation of the wall shear stresses in an abdominal aorta aneurysm (AAA) underprescribed geometrical changes. Additionally, computing on the grid infrastructure was performed to improve efficiency, since thousands of CFD runs were needed for creating machine learning data. We used several DM techniques and found that our DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data.
This paper introduces an automated methodology for the extraction of fetal heart rate from cutaneous potential abdominal electrocardiogram (abdECG) recordings. A three-stage methodology is proposed. Having the initial recording, which consists of a small number of abdECG leads in the first stage, the maternal R-peaks and fiducial points (QRS onset and offset) are detected using time-frequency (t-f) analysis and medical knowledge. Then, the maternal QRS complexes are eliminated. In the second stage, the positions of the candidate fetal R-peaks are located using complex wavelets and matching theory techniques. In the third stage, the fetal R-peaks, which overlap with the maternal QRS complexes (eliminated in the first stage) are found using two approaches: a heuristic algorithm technique and a histogram-based technique. The fetal R-peaks detected are used to calculate the fetal heart rate. The methodology is validated using a dataset of eight short and ten long-duration recordings, obtained between the 20th and the 41st week of gestation, and the obtained accuracy is 97.47%. The proposed methodology is advantageous, since it is based on the analysis of few abdominal leads in contrast to other proposed methods, which need a large number of leads.
In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation.
Cardiac ablation involves the risk of serious complications when thermal injury to the esophagus occurs. This paper proposes to reduce the risk of such injuries by a proactive visualization technique, improving physician awareness of the esophagus location in the absence of or in addition to a reactive monitoring device such as a thermal probe. This is achieved by combining a graphical representation of the esophagus with live fluoroscopy. Toward this goal, we present an automated method to reconstruct and visualize a 3-D esophagus model from fluoroscopy image sequences acquired using different C-arm viewing directions. In order to visualize the esophagus under fluoroscopy, it is first biomarked by swallowing a contrast agent such as barium. Images obtained in this procedure are then used to automatically extract the 2-D esophagus silhouette and reconstruct a 3-D surface of the esophagus internal wall. Once the 3-D representation has been computed, it can be visualized using fluoroscopy overlay techniques. Compared to 3-D esophagus imaging using CT or C-arm CT, our proposed fluoroscopy method requires low radiation dose and enables a simpler workflow on geometry-calibrated standard C-arm systems.
The radio frequency ablation segmentation tool (RFAST) is a software application developed using the National Institutes of Health's medical image processing analysis and visualization (MIPAV) API for the specific purpose of assisting physicians in the planning of radio frequency ablation (RFA) procedures. The RFAST application sequentially leads the physician through the steps necessary to register, fuse, segment, visualize, and plan the RFA treatment. Three-dimensional volume visualization of the CT dataset with segmented three dimensional (3-D) surface models enables the physician to interactively position the ablation probe to simulate burns and to semimanually simulate sphere packing in an attempt to optimize probe placement. This paper describes software systems contained in RFAST to address the needs of clinicians in planning, evaluating, and simulating RFA treatments of malignant hepatic tissue.
Compensatory fuzzy neural networks (CFNN) without normalization, which can be trained with a backpropagation learning algorithm, is proposed as a pattern recognition technique for intelligent detection of Doppler ultrasound waveforms of abnormal neonatal cerebral hemodynamics. Doppler ultrasound signals were recorded from the anterior cerebral arteries of 40 normal full-term babies and 14 mature babies with intracranial pathology. The features of normal and abnormal groups as inputs to pattern recognition algorithms were extracted from the maximum velocity waveforms by using principal component analysis. The proposed technique is compared with the CFNN with normalization and other pattern recognition techniques applied to Doppler ultrasound signals from various arteries. The results show that the proposed method is superior to the others, and can be a powerful technique to be used in analyzing Doppler ultrasound signals from various arteries.
The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated.
In this study, we developed an automated behavior analysis system using infrared (IR) motion sensors to assist the independent living of the elderly who live alone and to improve the efficiency of their healthcare. An IR motion-sensor-based activity-monitoring system was installed in the houses of the elderly subjects to collect motion signals and three different feature values, activity level, mobility level, and nonresponse interval (NRI). These factors were calculated from the measured motion signals. The support vector data description (SVDD) method was used to classify normal behavior patterns and to detect abnormal behavioral patterns based on the aforementioned three feature values. The simulation data and real data were used to verify the proposed method in the individual analysis. A robust scheme is presented in this paper for optimally selecting the values of different parameters especially that of the scale parameter of the Gaussian kernel function involving in the training of the SVDD window length, T of the circadian rhythmic approach with the aim of applying the SVDD to the daily behavior patterns calculated over 24 h. Accuracies by positive predictive value (PPV) were 95.8% and 90.5% for the simulation and real data, respectively. The results suggest that the monitoring system utilizing the IR motion sensors and abnormal-behavior-pattern detection with SVDD are effective methods for home healthcare of elderly people living alone.
Usage of compressed ECG for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size. However, conventional ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be performed. This added step of decompression before performing diagnosis for every ECG packet introduces unnecessary delay, which is undesirable for cardiovascular diseased (CVD) patients. In this paper, we are demonstrating an innovative technique that performs real-time classification of CVD. With the help of this real-time classification of CVD, the emergency personnel or the hospital can automatically be notified via SMS/MMS/e-mail when a life-threatening cardiac abnormality of the CVD affected patient is detected. Our proposed system initially uses data mining techniques, such as attribute selection (i.e., selects only a few features from the compressed ECG) and expectation maximization (EM)-based clustering. These data mining techniques running on a hospital server generate a set of constraints for representing each of the abnormalities. Then, the patient's mobile phone receives these set of constraints and employs a rule-based system that can identify each of abnormal beats in real time. Our experimentation results on 50 MIT-BIH ECG entries reveal that the proposed approach can successfully detect cardiac abnormalities (e.g., ventricular flutter/fibrillation, premature ventricular contraction, atrial fibrillation, etc.) with 97% accuracy on average. This innovative data mining technique on compressed ECG packets enables faster identification of cardiac abnormality directly from the compressed ECG, helping to build an efficient telecardiology diagnosis system.
Signal segmentation and classification of fluorescence in situ hybridization (FISH) images are essential for the detection of cytogenetic abnormalities. Since current methods are limited to dot-like signal analysis, we propose a methodology for segmentation and classification of dot and non-dot-like signals. First, nuclei are segmented from their background and from each other in order to associate signals with specific isolated nuclei. Second, subsignals composing non-dot-like signals are detected and clustered to signals. Features are measured to the signals and a subset of these features is selected representing the signals to a multiclass classifier. Classification using a naive Bayesian classifier (NBC) or a multilayer perceptron is accomplished. When applied to a FISH image database, dot and non-dot-like signals were segmented almost perfectly and then classified with accuracy of approximately 80% by either of the classifiers.
The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The assessment of the method using receiver operating characteristic analysis demonstrates improvement in image segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).
This paper describes the development of an interactive multimedia computer program to provide realistic training for responding to bomb threats targeted at abortion clinics. Because the simulator is computer-based, clinic administrators can receive training that might not otherwise be possible due to time, cost, or operational limitations.
Four-dimensional (4-D) imaging to capture the three-dimensional (3-D) structure and motion of the heart in real time is an emerging trend. We present here our method of interactive multiplanar reformatting (MPR), i.e., the ability to visualize any chosen anatomical cross section of 4-D cardiac images and to change its orientation smoothly while maintaining the original heart motion. Continuous animation to show the time-varying 3-D geometry of the heart and smooth dynamic manipulation of the reformatted planes, as well as large image size (100-300 MB), make MPR challenging. Our solution exploits the hardware acceleration of 3-D texture mapping capability of high-end commercial PC graphics boards. Customization of volume subdivision and caching concepts to periodic cardiac data allows us to use this hardware effectively and efficiently. We are able to visualize and smoothly interact with real-time 3-D ultrasound cardiac images at the desired frame rate (25 Hz). The developed methods are applicable to MPR of one or more 3-D and 4-D medical images, including 4-D cardiac images collected in a gated fashion.
Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.
Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson's disease (PD), postural instability being one of PD's major symptoms. The aim of this study was to assess the feasibility of using accelerometers to characterize the postural behavior of early mild PD subjects. Twenty PD and 20 control subjects, wearing an accelerometer on the lower back, were tested in five conditions characterized by sensory and attentional perturbation. A total of 175 measures were computed from the signals to quantify tremor, acceleration, and displacement of body sway. Feature selection was implemented to identify the subsets of measures that better characterize the distinctive behavior of PD and control subjects. It was based on different classifiers and on a nested cross validation, to maximize robustness of selection with respect to changes in the training set. Several subsets of three features achieved misclassification rates as low as 5%. Many of them included a tremor-related measure, a postural measure in the frequency domain, and a postural displacement measure. Results suggest that quantitative posture analysis using a single accelerometer and a simple test protocol may provide useful information to characterize early PD subjects. This protocol is potentially usable to monitor the disease's progression.
With the growing availability of health information on the Web, people are becoming more knowledgeable on their health conditions and treatment options, and more patients seek specialists by themselves. To aid patients in requesting self-referrals, we have developed and evaluated a web-based self-referral system in three specialty clinics at the University of Washington. Two clinics adopted the system for routine clinical use, while the third clinic decided not to. A major difference between these two groups was in how fast online requests from patients were handled, which significantly influenced patients' satisfaction. Clinic's preparedness for handling the temporarily increased workload due to the introduction of a new health information system played a role as well. Also, we noticed that the physician leadership/championship made a difference in the acceptance of our system.
Emerging technologies are transforming the workflows in healthcare enterprises. Computing grids and handheld mobile/wireless devices are providing clinicians with enterprise-wide access to all patient data and analysis tools on a pervasive basis. In this paper, emerging technologies are presented that provide computing grids and streaming-based access to image and data management functions, and system architectures that enable pervasive computing on a cost-effective basis. Finally, the implications of such technologies are investigated regarding the positive impacts on clinical workflows.
In this paper, we propose a new approach for accessing the electronical health records (EHR), and we apply it to the cardiology medical specialty. Though the use of EHR improves the storage and access to the information in it regarding the previous health records in papers, it entails the risk of having the same problems of huge size and of becoming inoperative and really difficult to handle, especially if the user is looking for a specific data item. Our proposal is based on the contextualization of the access, providing the user with the most important information for the assistance act in which he/she is involved. To do this, we define the set of possible contexts and consider different aspects of the pertinence of the documents to each context. We do it by using fuzzy logic and pay special attention to the efficiency, due to the huge size of the involved databases. Our proposal does not limit the access to the EHR, but establishes a prioritization based on the access needs, which provides the system with an additional advantage, easily enabling the use of new terminals and devices like tablet PCs and PDAs, which have great limitations in the interfaces.
Bias artifact corrupts MRIs in such a way that the image is afflicted by illumination variations. Some of the authors proposed the exponential entropy-driven homomorphic unsharp masking ( E(2)D-HUM) algorithm that corrects this artifact without any a priori hypothesis about the tissues or the MRI modality. Moreover, E(2)D-HUM does not care about the body part under examination and does not require any particular training task. People who want to use this algorithm, which is Matlab-based, have to set their own computers in order to execute it. Furthermore, they have to be Matlab-skilled to exploit all the features of the algorithm. In this paper, we propose to make such algorithm available as a service on a grid infrastructure, so that people can use it almost from everywhere, in a pervasive fashion, by means of a suitable user interface running on smartphones. The proposed solution allows physicians to use the E(2)D-HUM algorithm (or any other kind of algorithm, given that it is available as a service on the grid), being it remotely executed somewhere in the grid, and the results are sent back to the user's device. This way, physicians do not need to be aware of how to use Matlab to process their images. The pervasive service provision for medical image enhancement is presented, along with some experimental results obtained using smartphones connected to an existing Globus-based grid infrastructure.
The design of proper models for authorization and access control for electronic patient record (EPR) is essential to a wide scale use of EPR in large health organizations. In this paper, we propose a contextual role-based access control authorization model aiming to increase the patient privacy and the confidentiality of patient data, whereas being flexible enough to consider specific cases. This model regulates user's access to EPR based on organizational roles. It supports a role-tree hierarchy with authorization inheritance; positive and negative authorizations; static and dynamic separation of duties based on weak and strong role conflicts. Contextual authorizations use environmental information available at access time, like user/patient relationship, in order to decide whether a user is allowed to access an EPR resource. This enables the specification of a more flexible and precise authorization policy, where permission is granted or denied according to the right and the need of the user to carry out a particular job function.
We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.
In recent years, a relationship has been suggested between the occurrence of cerebral embolism and stroke. Ultrasound has therefore become essential in the detection of emboli when monitoring cerebral vascular disorders and forms part of ultrasound brain-imaging techniques. Such detection is based on investigating the middle cerebral artery using a TransCranial Doppler (TCD) system, and analyzing the Doppler signal of the embolism. Most of the emboli detected in practical experiments are large emboli because their signatures are easy to recognize in the TCD signal. However, detection of small emboli remains a challenge. Various approaches have been proposed to solve the problem, ranging from the exclusive use of expert human knowledge to automated collection of signal parameters. Many studies have recently been performed using time-frequency distributions and classical parameter modeling for automatic detection of emboli. It has been shown that autoregressive (AR) modeling associated with an abrupt change detection technique is one of the best methods for detection of microemboli. One alternative to this is a technique based on taking expert knowledge into account. This paper aims to unite these two approaches using AR modeling and expert knowledge through a neurofuzzy approach. The originality of this approach lies in combining these two techniques and then proposing a parameter referred to as score ranging from 0 to 1. Unlike classical techniques, this score is not only a measure of confidence of detection but also a tool enabling the final detection of the presence or absence of microemboli to be performed by the practitioner. Finally, this paper provides performance evaluation and comparison with an automated technique, i.e., AR modeling used in vitro.
Most of the commercial medical image viewers do not provide scalability in image compression and/or Region of Interest (ROI) encoding/decoding. Furthermore, these viewers do not take into consideration the special requirements and needs of a heterogeneous radio setting that is constituted by different access technologies (e.g., GPRS/UMTS, WLAN and DVB-H). The paper discusses a medical application that contains a viewer for DICOM images as a core module. The proposed application enables scalable wavelet-based compression, retrieval and decompression of DICOM medical images and also supports ROI coding/decoding. Furthermore, the presented application is appropriate for use by mobile devices activating in heterogeneous radio settings. In this context, performance issues regarding the usage of the proposed application in the case of a prototype heterogeneous system setup are also discussed.
This paper reports on original results of the Advancing Clinico-Genomic Trials on Cancer integrated project focusing on the design and development of a European biomedical grid infrastructure in support of multicentric, postgenomic clinical trials (CTs) on cancer. Postgenomic CTs use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypothesis in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. This paper provides a presentation of the needs of users involved in postgenomic CTs, and presents such needs in the form of scenarios, which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. Also, the main technological challenge, i.e. the design and development of semantically rich grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented, and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.
This communication describes a functioning model that permits access to an electronic health record across a small number of providers resident in an Australian regional setting. Design criteria designated that provider access rights were to be assignable, revokable, transportable, and informable.
Hospital workers are highly mobile; they are constantly changing location to perform their daily work, which includes visiting patients, locating resources, such as medical records, or consulting with other specialists. The information required by these specialists is highly dependent on their location. Access to a patient's laboratory results might be more relevant when the physician is near the patient's bed and not elsewhere. We describe a location-aware medical information system that was developed to provide access to resources such as patient's records or the location of a medical specialist, based on the user's location. The system is based on a handheld computer which includes a trained backpropagation neural-network used to estimate the user's location and a client to access information from the hospital information system that is relevant to the user's current location.
A model-based algorithm, termed exclusion region and position refinement (ERPR), is presented for improving the accuracy and repeatability of estimating the locations where vascular structures branch and cross over, in the context of human retinal images. The goal is two fold. First, accurate morphometry of branching and crossover points (landmarks) in neuronal/vascular structure is important to several areas of biology and medicine. Second, these points are valuable as landmarks for image registration, so improved accuracy and repeatability in estimating their locations and signatures leads to more reliable image registration for applications such as change detection and mosaicing. The ERPR algorithm is shown to reduce the median location error from 2.04 pixels down to 1.1 pixels, while improving the median spread (a measure of repeatability) from 2.09 pixels down to 1.05 pixels. Errors in estimating vessel orientations were similarly reduced from 7.2 degrees down to 3.8 degrees.
The quantitative assessment of and compensation for catheter rotation in intravascular ultrasound images presents a fundamental problem for noninvasive characterization of the mechanical properties of the coronary arteries. A method based on the scale-space optical flow algorithm with a feature-based weighting scheme is proposed to account for the aforementioned artifact. The computed vector field, describing the misalignment between two consecutive frames, allows the quantitative assessment of the amount of vessel wall tissue motion, which is directly related to the catheter rotation. Algorithm accuracy and robustness were demonstrated on two tissue-mimicking phantoms, subjected to controlled amount of angular deviation. The proposed method shows a great reliability in the prediction of catheter rotational motion up to 4 degrees.