Towards a generalized scheme for QRS detection in ECG waveforms
Signal Processing
QRS detection through time recursive prediction techniques
Signal Processing
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Neural Computing and Applications
Computer Methods and Programs in Biomedicine
Pattern Recognition
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
Computer Methods and Programs in Biomedicine
Features for analysis of electrocardiographic changes in partial epileptic patients
Expert Systems with Applications: An International Journal
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This paper presents an entropy-based algorithm for the detection of QRS complexes (cardiac beat) in the 12-lead Electrocardiogram (ECG) using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. Entropy criterion is used to enhance the QRS complexes. SVM is used as a classifier to delineate QRS and non-QRS regions. The performance of the proposed algorithm was tested using 12-lead real ECG recordings from the standard CSE ECG database. The numerical results indicated that the algorithm achieved 99.93% of detection rate. The percentage of false positive and false negative is 0.87% and 0.06%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database.