Catálogo de publicaciones - libros

Recent research trends focus on how multiscale biomedical information can be modeled and transformed into knowledge, in order to lead to a less interfering but also more individualized diagnosis and therapy. In order to assess the clinical importance of models of human pathology (e.g. cancer), it is necessary to validate them with prior and post treatment clinical data which in turn requires the determination of the tumor size and shape with high resolution, accuracy and precision, as well as structural and physiological information. This paper discusses some of the most important image analysis challenges in order to define an optimal method for extracting more accurate and precise anatomical and functional information related to the underlying pathology, which can be used for initializing and validating models of pathophysiology as well as simulations/predictions of the response to therapeutical regimes.

Since the 2001 Institute of Medicine Report which estimated that 44,000 to 98,000 patients die each year as a result of healthcare error. This report in effect launched a global patient safety movement, with many proposed regulatory, research and administrative solutions. Patient safety areas of focus such as work complexity, teamwork and communication, technology, and evidence based practice provide a basis for understanding healthcare error. Reliability concepts are the goal of healthcare organizations; and applications such as simulation theory provide means to achieve this status. The translation of research into practice is the foundation of organizational patient safety. Understanding and awareness of patient safety issues has increased; however, significant work to improve patient care outcomes remains.

The newly developed method for medical noisy data segmentation for the purpose of presentation and supporting the diagnostics is introduced. It also allows for morphometry and visualization of medical multimodal and dynamical data. A general mathematical framework is presented and characterized together with numerous applications. As this tool is designed to support human-computer interaction by means of involving the sense of sight, and suspected to be worthy in the virtual environment sensitive to the sense of touch, the discussion is supported with numerous examples of visualizations and multimodal and multidimensional applications of proposed method.

In this paper, we aim to classify surface electromyography (sEMG) by using Attribute Bagging-Radial Basis Function (AB-RBF) hybrid classifier. Eight normally-limbed individuals were recruited to participate in the experiments. Each subject was instructed to perform six kinds of finger movements and each movement was repeated 50 times. Features were extracted using wavelet transform and used to train the RBF classifier and the AB-RBF hybrid classifier. The experiment results showed that AB-RBF hybrid classifier achieved higher discrimination accuracy and stability than single RBF classifier. It proves that integrating classifiers using random feature subsets is an effective method to improve the performance of the pattern recognition system.

Epilepsy is the most common neurological disorder in the world, second only to stroke. There are nearly 15 million patients suffer from refractory epilepsy, with no available therapy. Although most seizures are not life threatening, they are an unpredictable source of annoyance and embarrassment, which will result in unconfident and fear. Prediction of epileptic seizures has a profound effect in understanding the mechanism of seizure, improving the rehabilitation possibilities and thereby the quality of life for epilepsy patients. A seizure prediction system can help refractory patients rehabilitate psychologically. In this paper, we introduce an epilepsy seizure prediction algorithm from scalp EEG based on morphological filter and Kolmogorov complexity. Firstly, a complex filter is constructed to remove the artifacts in scalp EEG, in which a morphological filter with optimized structure elements is proposed to eliminate the ocular artifact. Then, the improved Kolmogorov complexity is applied to describe the non-linear dynamic transition of brains. Results show that only the Kolmogorov complexity of electrodes near the epileptogenic focus reduces significantly before seizures. Through the analysis of 7 long-term scalp EEG recordings from 5 epilepsy patients, the average prediction time is 8.5 minutes, the mean sensitivity is 74.0% and specificity is 33.6%.

This paper describes a new virtual dynamic dentomaxillofacial system. Mechanical articulators have been used to reproduce mandibular movements and analyze occlusal contacts. With the help of VR and visualization technologies, virtual articulator systems can provide dynamic simulation and quantitative information visualization, enhance the functionality, and extend the system to additional application areas. We integrate mandibular movement simulation, occlusal analysis and TMJ analysis into our system, and design new algorithms to improve the results of analysis. This system is helpful to the education, the research, and the clinic in dentistry. An evaluation is conducted to prove the functionality and usability of the system.

In contrast to the previous studies that have dealt with phenomenon of coherence resonance induced by external noise in excitable neuron model, in this paper the mechanism of bifurcation-dependent coherence resonance (CR) of excitable neuron model is investigated by researching the random transitions of system motion between attractors in the two sides of bifurcation point. The results of research show: For two excitable neuron model, FHN neuron model and HR neuron model, There exist different attractors in two sides of the two excitable neuron model Hopf bifurcation point, at the presence of internal or external noise the system motion may transit between attractors in two sides of bifurcation point; mechanism of bifurcation-dependent CR of excitable neuron model is related to this kind of random transitions, the frequency of transitions tend towards a certain frequency for a certain optimal noise intensity, and the signal-noise-ratio of system response evaluated at this certain frequency is maximal at the optimal noise intensity, CR occurs.

A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.

Until now DHMs are especially used to design the dimensions of products and production assembly according to anthropometric demands. Recently DHMs are additionally equipped with strengths simulation so that also the dimensioning of reaction forces is possible. First steps are done to describe and evaluate the contact between human body and environment. Some examples will be shown. However in this area further important steps are necessary. Especially the self paced calculation of posture depending on this contact is to be realized. Some proposals exist for the contact of seat and body. Also first basic research is done in order to simulate motion behavior. Especially the detection of “leading body elements” as basic idea for this simulation can be seen as an initial step to generate modeling of cognitive human properties. However, in order to realize it the simulation of the properties of sense organs is necessary. Certain properties of the eyes can be simulated rather simple. Meanwhile some experience exits to understand the glance behavior depending on specific tasks (e.g. car driving). That can serve as basic for input to cognitive models. The output of these can be the track in space of the leading body element. On the other hand sensor organs properties in the field of hearing and climate are possible. In both cases the more difficult problem is to simulate the properties of the environment. General application field of these future development is the computer aided ergonomic design of workplaces in production lines and of products especially vehicles already in the definition and development phase. In this connection is to be considered that in future especially the design of information flow in these areas becomes dominant. An example is the growing development of assistance systems in cars. The application of DHMs will allow achieving the connection between information design and the necessary geometric design of the equipment.

Based on Neural Mechanism theory of short-term memory, a predigested model was established. It demonstrated learning, establishment and strengthening process concerning with the motor pattern domination of high-level nerve center system. According to the model three feedback loops and their dialectic correlation were synthetically analyzed during rowing athletes studied the technique in training. The analyzed results provided the technical requests and measuring parameters of the system, then, a visual feedback system for rowing was designed and developed. This system has been primarily used in the national rowing team.