Towards a generalized scheme for QRS detection in ECG waveforms
Signal Processing
QRS detection through time recursive prediction techniques
Signal Processing
Syntactic Pattern Recognition of the ECG
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database
Computers and Biomedical Research
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Neural Computing and Applications
Computer Methods and Programs in Biomedicine
Pattern Recognition
Novel Approach to Fuzzy-Wavelet ECG Signal Analysis for a Mobile Device
Journal of Medical Systems
A New QRS Detection Method Using Wavelets and Artificial Neural Networks
Journal of Medical Systems
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A method based on signal entropy is proposed for the detection of QRS complexes in the 12-lead electrocardiogram (ECG) using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander in the ECG signal. Combined Entropy criterion was 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 0.54% and 0.06%, respectively. The proposed algorithm performs better as compared with published results of other QRS detectors tested on the same database.