Title :
Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification
Author :
Can Ye ; Vijaya Kumar, B.V.K. ; Coimbra, M.T.
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small amount of individual data, which is a binary one-against-the-rest classifier, providing discrimination between normal and abnormal patterns from that individual. Such an individual-specific classifier can be a two-class classifier or a one-class classifier, depending on the availability of abnormal patterns in the individual training dataset. The classifications from the two classifiers are fused to obtain a final decision. The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods. The proposed method can also be useful in anomaly detection of other biomedical signals.
Keywords :
electrocardiography; medical signal processing; signal classification; signal detection; support vector machines; abnormal pattern discrimination; anomaly detection; binary one-against-the-rest classifier; biomedical signals; cardiac arrhythmias; customized ECG signals heartbeat classification; electrocardiogram; general multiclass classifiers; global training dataset; incremental support vector machine method; normal pattern discrimination; specific two-class classifiers; Databases; Electrocardiography; Heart beat; Support vector machines; Testing; Training; Training data;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4