Lists the reviewers who contributed to IEEE Transactions on Information Technology in Biomedicine.
The paper presents a new approach to detection of apnea/hypopnea events, in the presence of artifacts and breathing irregularities, from a single-channel airflow record. The proposed algorithm, based on a robust envelope detector, identifies segments of signal affected by a high amplitude modulation corresponding to apnea/hypopnea events. It is shown that a robust airflow envelope-free of breathing artifacts-improves effectiveness of the diagnostic process and allows one to localize the beginning and the end of each episode more accurately. The performance of the proposed approach, evaluated on 30 overnight polysomnographic recordings, was assessed using diagnostic measures such as accuracy, sensitivity, specificity, and Cohen's coefficient of agreement; the achieved levels were equal to 95%, 90%, 96%, and 0.82, respectively. The results suggest that the algorithm may be implemented successfully in portable monitoring devices, as well as in software-packages used in sleep laboratories for automated evaluation of sleep apnea/hypopnea syndrome.
Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal before medical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient.
This paper presents a systematic approach to system identification and closed-loop control of end-tidal carbon dioxide partial pressure (PETCO2) in mechanically ventilated patients. An empirical model consisting of a linear dynamic system followed by an affine transform is proposed to derive a low-order and high-fidelity representation that can reproduce the positive and inversely proportional dynamic input-output relationship between PETCO2 and minute ventilation in mechanically ventilated patients. The predictive capability of the empirical model was evaluated using experimental respiratory data collected from 18 mechanically ventilated human subjects. The model predicted PETCO2 response accurately with a root-mean-squared error of 0.22 ± 0.16 mmHg and a coefficient of determination r2 of 0.81 ± 0.18 (mean ± SD) when a second-order rational transfer function was used as its linear dynamic component. Using the proposed model, a closed-loop control method for PETCO2 based on a proportional-integral (PI) compensator was proposed by systematic analysis of the system root locus. For the 18 mechanically ventilated patient models identified, the PI compensator exhibited acceptable closed-loop response with a settling time of 1.27 ± 0.20 min and a negligible overshoot (0.51 ± 1.17%), in addition to zero steady-state PETCO2 set point tracking. The physiologic implication of the proposed empirical model was analyzed by comparing it with the traditional multicompartmental model widely used in pharmacological modeling.
A plethora of digital ECG formats have been proposed and implemented. This heterogeneity hinders the design and development of interoperable systems and entails critical integration issues for the healthcare information systems. This paper aims at performing a comprehensive overview on the current state of affairs of the interoperable exchange of digital ECG signals. This includes 1) a review on existing digital ECG formats, 2) a collection of applications and cardiology settings using such formats, 3) a compilation of the relationships between such formats, and 4) a reflection on the current situation and foreseeable future of the interoperable exchange of digital ECG signals. The objectives have been approached by completing and updating previous reviews on the topic through appropriate database mining. 39 digital ECG formats, 56 applications, tools or implantation experiences, 47 mappings/converters, and 6 relationships between such formats have been found in the literature. The creation and generalization of a single standardized ECG format is a desirable goal. However, this unification requires political commitment and international cooperation among different standardization bodies. Ongoing ontology-based approaches covering ECG domain have recently emerged as a promising alternative for reaching fully fledged ECG interoperability in the near future.
A system for unobtrusive night-time electrocardiogram (EKG) and heart rate variability (HRV) monitoring as well as data analysis methods are presented, comparing bed sheet HR and HRV values with corresponding parameters obtained by a reference measurement. Our system uses eight embroidered textile electrodes attached laterally to a bed sheet for measuring bipolar contact EKG from multiple channels. The electrodes are arranged in a line so that at least two adjacent electrodes make sufficient skin contact. The focus of the signal processing development has been on selecting the best measurement channel for further analysis and minimizing the amount of incorrectly detected R-peaks. The test measurements were performed with four healthy men without previously known cardiac disorders and one who frequently had premature ventricular contractions (ectopic beats). For healthy test subjects, an average of 94.9% heartbeat detection coverage was achieved with the system during 29 measurement nights (in total 213.8 h of data). In most cases, the quality of the signal obtained from bed sheet electrodes is good enough for the computer-assisted cardiac arrhythmia detection. Applications for EKG derived RR-interval data include the calculation of HRV parameters that can be utilized in sleep quality analysis and other wellness-related topics as well as sleep apnoea detection.
Childhood obesity is nowadays considered one of the major health problems that many societies suffer from today. The obesity epidemic leads to several life threatening conditions such as diabetes, heart disease, high blood pressure, and mental health problems like depression, anxiety, and loneliness just to mention a few. Several approaches, including physical exercises, strict diet, and exergames among others, have been adopted to address the obesity epidemic. Exergames are considered the innovative approach for fighting several health problems such as obesity, where a combination of “exercise” and 3-D “gaming” are proposed to incite kids to exercise as a team. Collaborative exergaming became even more popular given that it addresses the social side of the obesity epidemic, and it motivates kids to socialize with other kids. Traditional exergames are based on the client-server approach where the server is responsible for streaming the 3-D environment. However, this can lead to latency and server bottleneck if many clients participate in the exergame, which leads to the kids stopping exercising. Having an exergame application that does not suffer from networking problem such as delay, is very important given that it increases the exercise hours. In this paper, we propose a new trend of mobile collaborative exergaming applications that is based on the peer-to-peer architecture, as well as two supplying partner selection protocols that aim at selecting the suitable source responsible for streaming the relevant 3-D data. Our system, that we refer to as MOSAIC, is intended for mobile collaborative exergames that incite kids to move inside a large area, using thin mobile devices such as head-mounted devices, have physical exercises, and collaborate with other kids which in consequence address several health problems such as the obesity epidemic on the physical and social plans. Our proposed mobile collaborative exergame aims at inciting the kids to exercise as a team for a longer time by improving the quality of the streaming and reducing the delay. This is accomplished by our proposed supplying partner selection protocols that provide a quick discovery of multiple supplying partners, by minimizing the time required to acquire the data. The performance evaluation that we have obtained to evaluate our suite of protocols using a realistic set of exergame scenarios for obese kids is then presented and discussed.
In this paper, we studied the improvement in heartbeat classification achieved by including information from multilead ECG recordings in a previously developed and validated classification model. This model includes features from the RR interval series and morphology descriptors for each lead calculated from the wavelet transform. The experiments were carried out in the INCART database, available in Physionet, and the generalization was corroborated in private and public databases. In all databases, the AAMI recommendations for class labeling and results presentation were followed. Different strategies to integrate the additional information available in the 12-leads were studied. The best performing strategy consisted in performing principal component analysis to the wavelet transform of the available ECG leads. The performance indices obtained for normal beats were sensitivity (S) 98%, positive predictive value (P + ) 93%; for supraventricular beats, (S) 86%, (P + ) 91%; and for ventricular beats (S) 90%, (P + ) 90%. The generalization capability of the chosen strategy was confirmed by applying the classifier to other databases with different number of leads with comparable results. In conclusion, the performance of the reference two-lead classifier was improved by taking into account additional information from the 12-leads.
Genomic biomarkers are essential for understanding the underlying molecular basis of human diseases such as cardiovascular disease. In this review, we describe a biomarker identification pipeline for cardiovascular disease, which includes 1) high-throughput genomic data acquisition, 2) preprocessing and normalization of data, 3) exploratory analysis, 4) feature selection, 5) classification, and 6) interpretation and validation of candidate biomarkers. We review each step in the pipeline, presenting current and widely used bioinformatics methods. Furthermore, we analyze several publicly available cardiovascular genomics datasets to illustrate the pipeline. Finally, we summarize the current challenges and opportunities for further research.
An autonomous task-prompting system is presented to increase workplace and life independence for people with cognitive impairments such as traumatic brain injury, intellectual disability, schizophrenia, and down syndrome. This paper describes an approach to providing distributed cognition support of work engagement for persons with cognitive disabilities. In the pilot study, a prototype was built and tested in a community-based rehabilitation program involving preservice food preparation training of eight participants with cognitive impairments. The results show improvement in helping with task engagement is statistically significant compared to the oral-instruction method. A follow-up comparative study with two participants evaluated the shadow-team approach against the proposed system. Although the number of participants was few, the participants were studied in depth and the findings were very promising. The results in the autonomous task prompting without staff intervention indicate that the performance is statistically as good as the shadow-team approach. Our findings suggest that acquisition of job skills may be facilitated by the proposed system in conjunction with operant conditioning strategies.
Mobile technologies are increasingly important components in telemedicine systems and are becoming powerful decision support tools. Universal access to data may already be achieved by resorting to the latest generation of tablet devices and smartphones. However, the protocols employed for communicating with image repositories are not suited to exchange data with mobile devices. In this paper, we present an extensible approach to solving the problem of querying and delivering data in a format that is suitable for the bandwidth and graphic capacities of mobile devices. We describe a three-tiered component-based gateway that acts as an intermediary between medical applications and a number of Picture Archiving and Communication Systems (PACS). The interface with the gateway is accomplished using Hypertext Transfer Protocol (HTTP) requests following a Representational State Transfer (REST) methodology, which relieves developers from dealing with complex medical imaging protocols and allows the processing of data on the server side.
The development of medical sensor networks (MSNs) is imperative for e-healthcare, but security remains a formidable challenge yet to be resolved. Traditional cryptographic mechanisms do not suffice given the unique characteristics of MSNs, and the fact that MSNs are susceptible to a variety of node misbehaviors. In such situations, the security and performance of MSNs depend on the cooperative and trust nature of the distributed nodes, and it is important for each node to evaluate the trustworthiness of other nodes. In this paper, we identify the unique features of MSNs and introduce relevant node behaviors, such as transmission rate and leaving time, into trust evaluation to detect malicious nodes. We then propose an application-independent and distributed trust evaluation model for MSNs. The trust management is carried out through the use of simple cryptographic techniques. Simulation results demonstrate that the proposed model can be used to effectively identify malicious behaviors and thereby exclude malicious nodes. This paper also reports the experimental results of the Collection Tree Protocol with the addition of our proposed model in a network of TelosB motes, which show that the network performance can be significantly improved in practice. Further, some suggestions are given on how to employ such a trust evaluation model in some application scenarios.
Traditional cephalometry with a cast-mounted articulator is a useful and well-established tool for orthognathic surgery planning. However, 2-D planning with dental casts cannot provide comprehensive information on facial bone conditions, especially with regards to symmetry. To plan and predict postsurgical facial symmetry and occlusions, this paper uses an optical navigation system to track the movement of the upper and lower dental models in model surgery. The corresponding movement and the new position of the jawbones are demonstrated in the computer and the symmetry status can be evaluated. Surgical splints can be fabricated from the virtual models and used in surgery. The procedure provides more realistic predictions, which can assist surgeons to better control postsurgical facial harmony.
In this paper, we present a novel two-step algorithm for segmentation of coronary arteries in computed tomography images based on the framework of active contours. In the proposed method, both global and local intensity information is utilized in the energy calculation. The global term is defined as a normalized cumulative distribution function, which contributes to the overall active contour energy in an adaptive fashion based on image histograms, to deform the active contour away from local stationary points. Possible outliers, such as kissing vessel artifacts, are removed in the postprocessing stage by a slice-by-slice correction scheme based on multiregion competition, where both arteries and kissing vessels are identified and tracked through the slices. The efficiency and the accuracy of the proposed technique are demonstrated on both synthetic and real datasets. The results on clinical datasets show that the method is able to extract the major branches of arteries with an average distance of 0.73 voxels to the manually delineated ground truth data. In the presence of kissing vessel artifacts, the outer surface of the entire coronary tree, extracted by the proposed algorithm, is smooth and contains fewer erroneous regions, originating in kissing vessel artifacts, as compared to the initial segmentation.
Mandibular angle reduction is a popular and efficient procedure widely used to alter the facial contour. The primary surgical instruments, the reciprocating saw, and the round burr, employed in the surgery have a common feature: operating at a high speed. Generally, inexperienced surgeons need a long-time practice to learn how to minimize the risks caused by the uncontrolled contacts and cutting motions in manipulation of instruments with high-speed reciprocation or rotation. A virtual reality-based surgical simulator for the mandibular angle reduction was designed and implemented on a compute unified device architecture (CUDA)-based platform in this paper. High-fidelity visual and haptic feedbacks are provided to enhance the perception in a realistic virtual surgical environment. The impulse-based haptic models were employed to simulate the contact forces and torques on the instruments. It provides convincing haptic sensation for surgeons to control the instruments under different reciprocation or rotation velocities. The real-time methods for bone removal and reconstruction during surgical procedures have been proposed to support realistic visual feedbacks. The simulated contact forces were verified by comparing against the actual force data measured through the constructed mechanical platform. An empirical study based on the patient-specific data was conducted to evaluate the ability of the proposed system in training surgeons with various experiences. The results confirm the validity of our simulator.
This paper presents the prototype implementation of a Bio-Patch using fully integrated low-power system-on-chip (SoC) sensor and paper-based inkjet printing technology. The SoC sensor is featured with programmable gain and bandwidth to accommodate a variety of biosignals. It is fabricated in a 0.18-μm standard CMOS technology, with a total power consumption of 20 μW from a 1.2 V supply. Both the electrodes and interconnections are implemented by printing conductive nanoparticle inks on a flexible photo paper substrate using inkjet printing technology. A Bio-Patch prototype is developed by integrating the SoC sensor, a soft battery, printed electrodes, and interconnections on a photo paper substrate. The Bio-Patch can work alone or operate along with other patches to establish a wired network for synchronous multiple-channel biosignals recording. The measurement results show that electrocardiogram and electromyogram are successfully measured in in vivo tests using the implemented Bio-Patch prototype.
The paper presents a subject-specific radio propagation study and system modeling in wireless body area networks using a simulation tool based on the parallel finite-difference time-domain technique. This technique is well suited to model the radio propagation around complex, inhomogeneous objects such as the human body. The impact of different digital phantoms in on-body radio channel and system performance was studied. Simulations were performed at the frequency of 3-10 GHz considering a typical hospital environment, and were validated by on-site measurements with reasonably good agreement. The analysis demonstrated that the characteristics of the on-body radio channel and system performance are subject-specific and are associated with human genders, height, and body mass index. Maximum variations of almost 18.51% are observed in path loss exponent due to change of subject, which gives variations of above 50% in system bit error rate performance. Therefore, careful consideration of subject-specific parameters are necessary for achieving energy efficient and reliable radio links and system performance for body-centric wireless network.
This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimized selection and integration of features derived from textural, border-based, and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundary-series model of the lesion border and analyzing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimized selection of features is achieved by using the gain-ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, support vector machine, random forest, logistic model tree, and hidden naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation, and test image sets. The system achieves an accuracy of 91.26% and area under curve value of 0.937, when 23 features are used. Other important findings include 1) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and 2) higher contribution of texture features than border-based features in the optimized feature set.
