The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Expert Systems with Applications: An International Journal
Novel Approach to Fuzzy-Wavelet ECG Signal Analysis for a Mobile Device
Journal of Medical Systems
Hybrid intelligent techniques for MRI brain images classification
Digital Signal Processing
Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias
Digital Signal Processing
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Diagnosis of cardiac arrhythmia using fuzzy immune approach
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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In this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system.