The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition Letters
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
PVC discrimination using the QRS power spectrum and self-organizing maps
Computer Methods and Programs in Biomedicine
Classification Methods with Reject Option Based on Convex Risk Minimization
The Journal of Machine Learning Research
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine
QRS detection based on wavelet coefficients
Computer Methods and Programs in Biomedicine
Multistage approach for clustering and classification of ECG data
Computer Methods and Programs in Biomedicine
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In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.