ECG arrhythmia recognition improvement using respiration information

In this paper we propose two ways to improve the accuracy of ECG signal recognition by filtering out the effect of the respiration in the ECG signal and by using the information of breathing stage as features in ECG signal classification. These approaches can improve the reliability and accuracy of the arrhythmia classification. As the classifier we use the modified neuro-fuzzy TSK network. The proposed solution will be tested with data from the MIT-BIH and the MGH/MF databases.