Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Artificial Neural Networks in Biomedicine
Artificial Neural Networks in Biomedicine
Two neural networks architectures for detecting AVB
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
Two neural networks architectures for detecting AVB
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases
Expert Systems with Applications: An International Journal
Biologically inspired evolutionary computing tools for the extraction of fetal electrocardiogram
WSEAS Transactions on Signal Processing
Discrimination of myocardial infarction stages by subjective feature extraction
Computer Methods and Programs in Biomedicine
Gastro-intestinal tract inspired computational model for myocardial infarction diagnosis
Expert Systems with Applications: An International Journal
Test-retest reliability and feature selection in physiological time series classification
Computer Methods and Programs in Biomedicine
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We use artificial neural networks (ANNs) to detect signs of acute myocardial infarction (AMI) in ECGs. The 12-lead ECG is decomposed into Hermite basis functions, and the resulting coefficients are used as inputs to the ANNs. Furthermore, we present a case-based method that qualitatively explains the operation of the ANNs, by showing regions of each ECG critical for ANN response. Key ingredients in this method are: (i) a cost function used to find local ECG perturbations leading to the largest possible change in ANN output and (ii) a minimization scheme for this cost function using mean field annealing. Our approach was tested on 2238 ECGs recorded at an emergency department. The obtained ROC areas for ANNs trained with the Hermite representation and standard ECG measurements were 83.4 and 84.3% (P=0.4), respectively. We believe that the proposed method has potential as a decision support system that can provide good advice for diagnosis, as well as providing the physician with insight into the reason underlying the advice.