Combining General Multi-class and Specific Two-class Classifiers for Improved Customized ECG Heartbeat Classification

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Ye, C (Author) Other; Kumar, BVKV (Author) Other; Coimbra, MT (Author) FCUP

Conference proceedings International

Title: 2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)

Pages: 2428-2431

21st International Conference on Pattern Recognition (ICPR)

Tsukuba, JAPAN, NOV 11-15, 2012

Indexing

ISI Web of Knowledge - 38 Citations

Scopus - 48 Citations

Other information

Authenticus ID: P-008-9Q0

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4