Classification of multichannel ECG signals using a cross-distance analysis

Presents a multi-stage algorithm for multichannel ECG beat classification into normal and abnormal categories using a sequential beat clustering and a cross-distance analysis algorithm. After the clustering stage, a search algorithm is applied to detect the main normal class. Then other clusters are classified based on their distance from the main normal class. The algorithm is developed for both 1-lead and 2-lead ECG. Evaluated results on MIT-BIH database exhibit a classification error of less than 1% for 1-lead and 0.2% for 2-lead and clustering error of 0.2%.