Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram

— Among all ECG components, QRS complex is the most significant feature. Entropy based method for the detection of QRS complexes (cardiac beat) in the single lead Electrocardiogram (ECG) is proposed in this paper. Digital filtering techniques are used to remove noise and base line wander in the ECG signal. Entropy criterion is used to enhance the QRS complexes. Support Vector Machine (SVM) is used as a classifier to delineate QRS and nonQRS regions. The performance of the algorithm is evaluated against the standard CSE ECG database. The numerical results indicated that the algorithm achieved 99.68% of the detection rate. The percentage of false positive and false negative is 2.28 and 0.32 respectively. The detection rate depends strongly on the quality of training, data representation and the mathematical basis of the classifier.

• Type: Conference
• Year: 2007
• Where: IMECS
• Authors: Sarabjeet Singh Mehta, Nitin Shivappa Lingayat
• Keywords: ECG Components, IMECS 2007, QRS Complexes, Standard Cse Ecg, Theoretical Computer Science