Neural Computation
Detection and Estimation Methods for Biomedical Signals
Detection and Estimation Methods for Biomedical Signals
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
A fuzzy approach to computer-assisted myocardial ischemia diagnosis
Artificial Intelligence in Medicine
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
Computers in Biology and Medicine
Discrimination of myocardial infarction stages by subjective feature extraction
Computer Methods and Programs in Biomedicine
Dimensionality reduction oriented toward the feature visualization for ischemia detection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Emerging patterns based methodology for prediction of patients with myocardial ischemia
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
An improved procedure for detection of heart arrhythmias with novel pre-processing techniques
Expert Systems: The Journal of Knowledge Engineering
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An automated technique was developed for the detection of ischemic episodes in long duration electrocardiographic (ECG) recordings that employs an artificial neural network. In order to train the network for beat classification, a cardiac beat dataset was constructed based on recordings from the European Society of Cardiology (ESC) ST-T database. The network was trained using a Bayesian regularisation method. The raw ECG signal containing the ST segment and the T wave of each beat were the inputs to the beat classification system and the output was the classification of the beat. The input to the network was produced through a principal component analysis (PCA) to achieve dimensionality reduction. The network performance in beat classification was tested on the cardiac beat database providing 90% sensitivity (Se) and 90% specificity (Sp). The neural beat classifier is integrated in a four-stage procedure for ischemic episode detection. The whole system was evaluated on the ESC ST-T database. When aggregate gross statistics was used the Se was 90% and the positive predictive accuracy (PPA) 89%. When aggregate average statistics was used the Se became 86% and the PPA 87%. These results are better than other reported.