Tele-affiliation in medical education: experience from the international program at Yale Office of Telemedicine.

OBJECTIVES: Telemedicine is developed in response to the needs of users that results in a more viable model. Yale has developed a process called tele-affiliation to combine services that are customized to the international client's needs. METHODS: Several defined steps compose the tele-affiliation process. The Yale-Greece telemedicine program is used as an illustration of this process. Some of the programs developed in response to Greek needs include breast cancer clinics, women's health clinics and tele-homecare monitoring for post-operative and chronically ill patients. RESULTS: Tele-affiliation creates on infrastructure that has the potential to change the method of health care delivery. By using the infrastructure created by the tele-affiliation process, templates for disease management, as well as health promotion and education can be delivered to a global audience. CONCLUSIONS: A tele-affiliation education environment has been developed and tested between Yale University School of Medicine and Greece resulting in an improved infrastructure for health education and management.

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

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 processing techniques based on neural networks and bidirectional associative memories.

Two ECG processing techniques are described for the classification of QRSs, PVCs and normal and ischaemic beats. The techniques use neural network (NN) technology in two ways. The first technique, uses nonlinear ECG mapping preprocessing and subsequently for classification uses a shrinking algorithm based on NNs. This technique is applied to the QRS/PVC problem with good result. The second technique is based on the Bidirectional Associative Memory (BAM) NN and is used to distinguish normal from ischaemic beats. In this technique the ECG beat is treated as a digitized image which is then transformed into a bipolar vector suitable for input in the BAM. The results show that this method, if properly calibrated, can result in a fast and reliable ischaemic beat detection algorithm.

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

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 (ECG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCU's).