Neural Networks
Original Contribution: Stacked generalization
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A connectionist method for pattern classification with diverse features
Pattern Recognition Letters
Neural Computing and Applications
Adaptive mixtures of local experts
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents
Computer Methods and Programs in Biomedicine
Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals
Expert Systems with Applications: An International Journal
Novel Approach to Fuzzy-Wavelet ECG Signal Analysis for a Mobile Device
Journal of Medical Systems
Analysis of human PPG, ECG and EEG signals by eigenvector methods
Digital Signal Processing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Sparse Representation-Based Heartbeat Classification Using Independent Component Analysis
Journal of Medical Systems
Visual data mining with self-organising maps for ventricular fibrillation analysis
Computer Methods and Programs in Biomedicine
Real-time CHF detection from ECG signals using a novel discretization method
Computers in Biology and Medicine
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In this paper, implementation of automated diagnostic systems with diverse and composite features for electrocardiogram (ECG) beats was presented and their accuracies were determined. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, better classification procedures were searched for ECG beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features were compared. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.