Showing papers by "Nicos Maglaveras published in 1998"

Hybrid image segmentation using watersheds and fast region merging

Abstract: A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottom-up) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images are presented.

ECG pattern recognition and classification using non-linear transformations and neural networks: A review

Abstract: The most widely used signal in clinical practice is the ECG. ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. In this paper, we shall review some current trends on ECG pattern recognition. In particular, we shall review non-linear transformations of the ECG, the use of principal component analysis (linear and non-linear), ways to map the transformed data into n-dimensional spaces, and the use of neural networks (NN) based techniques for ECG pattern recognition and classification. The problems we shall deal with are the QRS/PVC recognition and classification, the recognition of ischemic beats and episodes, and the detection of atrial fibrillation. Finally, a generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques. The performance measures of the sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH and the European ST-T databases.

ECG analysis using nonlinear PCA neural networks for ischemia detection

Abstract: The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats.

An adaptive backpropagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database

Abstract: A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm drastically reduces training time (tenfold decrease in our case) when compared to the classical BP algorithm. The recall phase of the NN is then extremely fast, a fact that makes it appropriate for real-time detection of ischemic episodes. The resulting NN is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% while the ischemia duration sensitivity is 72.22%. The results show that NN can be used in electrocardiogram (EGG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCUs).

An integrated environment for ECG processing.

Abstract: The huge amount of information involved in clinical cardiological examination raises the need for efficient patient data management and for the fusion of modern information processing techniques in the everyday clinical workstation. An integrated medical information system has been developed in order to organize the local cardiological legacy system in the AHEPA hospital. An ODBC based database (like Ms SqlServer or MSAccess) holds patient and ECG data. The system incorporates data management (storing and retrieval) and data processing modules. The processing module is added as an independent DLL. The visualization component results in a better view of the information. The components are integrated in a friendly window interface that lets the doctor browse patient information, apply modern signal processing techniques, make special measurements and store them in the database for research reasons.

Medical informatics education.

Abstract: Medical Informatics is a multidisciplinary field, dealing mainly with informatics and technology applications in health care Medical Informatics is composed from a number of sub-areas such as computer based patient record (CPR), processing of multimedia information (signals, images), coding and transmission through high speed networks of medical information (telematics), medical decision support systems, data security and integrity, integration of technologies in hospital and regional environments, and development of educational tools The people who receive such an education are capable of development, integration and maintenance of complex hospital and health information systems both at departmental and regional levels A very important issue however is the acceptance of information technology (IT) solutions engineered by medical informaticians from the medical personnel In this paper we shall deal with the set-up of a medical informatics and medical technology educational environment, as well as the areas from medical informatics that the average user needs to be familiar with in order for the successful deployment of IT solutions in health care

Smart alarming scheme for ICU using neural networks

Abstract: In this work a new scheme for intelligent alarming is presented. The idea is that in order for an alarming scheme to be able to be efficient, the definitions of normal, abnormal and intermediate state have to be changed many times on an hour to hour basis, since in ICU the patient state can change dramatically from day to day. In order to do so, unsupervised and supervised learning systems need to be incorporated that can be trained fast and reliably by the medical personnel. Thus the need for a system that can be trained fast and the existence of a user-friendly MMI where the doctor shall be able to modulate the boundaries between normal, abnormal and intermediate values according to the patient's condition is imperative. In this paper, this approach is implemented, using neural networks (NN) for training and learning, and a user friendly MMI using colours and 2-D phase planes of parameters monitored in ICU are used to achieve more efficient alarming schemes.