Artificial Intelligence in Medicine
Novel Approach to Fuzzy-Wavelet ECG Signal Analysis for a Mobile Device
Journal of Medical Systems
Evolvable block-based neural network design for applications in dynamic environments
VLSI Design - Special issue on selected papers from the midwest symposium on circuits and systems
Active learning methods for electrocardiographic signal classification
IEEE Transactions on Information Technology in Biomedicine
Expert Systems with Applications: An International Journal
Selection of effective features for ECG beat recognition based on nonlinear correlations
Artificial Intelligence in Medicine
Application of principal component analysis to ECG signals for automated diagnosis of cardiac health
Expert Systems with Applications: An International Journal
Electrocardiographic signal classification with evolutionary artificial neural networks
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Artificial Intelligence in Medicine
Time-frequency analysis of signals using support adaptive Hermite-Gaussian expansions
Digital Signal Processing
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This paper presents evolvable block-based neural networks (BbNNs) for personalized ECG heartbeat pattern classification. A BbNN consists of a 2-D array of modular component NNs with flexible structures and internal configurations that can be implemented using reconfigurable digital hardware such as field-programmable gate arrays (FPGAs). Signal flow between the blocks determines the internal configuration of a block as well as the overall structure of the BbNN. Network structure and the weights are optimized using local gradient-based search and evolutionary operators with the rates changing adaptively according to their effectiveness in the previous evolution period. Such adaptive operator rate update scheme ensures higher fitness on average compared to predetermined fixed operator rates. The Hermite transform coefficients and the time interval between two neighboring R-peaks of ECG signals are used as inputs to the BbNN. A BbNN optimized with the proposed evolutionary algorithm (EA) makes a personalized heartbeat pattern classifier that copes with changing operating environments caused by individual difference and time-varying characteristics of ECG signals. Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats (98.1%) and supraventricular ectopic beats (96.6%) patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.