Modular construction of time-delay neural networks for speech recognition
Neural Computation
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Nonlinear time series analysis
Nonlinear time series analysis
Machine Learning
On the use of orthogonal GMM in speaker recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
Time Series Classification Using Gaussian Mixture Models of Reconstructed Phase Spaces
IEEE Transactions on Knowledge and Data Engineering
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Statistical models of reconstructed phase spaces for signal classification
IEEE Transactions on Signal Processing - Part I
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively few of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, using phase space reconstruction in order to classify five heartbeat types can fill this gap to some extent. In the first and second method, Reconstructed phase space (RPS) is modeled by the Gaussian mixture model (GMM) and bins, respectively, and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before, for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% classification accuracy.