Active learning methods for electrocardiographic signal classification.
In this paper, we present three active learning strategies for the classification of electrocardiographic (ECG) signals. Starting from a small and suboptimal training set, these learning strategies select additional beat samples from a large set of unlabeled data. These samples are labeled manually, and then added to the training set. The entire procedure is iterated until the construction of a final training set representative of the considered classification problem. The proposed methods are based on support vector machine classification and on the: 1) margin sampling; 2) posterior probability; and 3) query by committee principles, respectively. To illustrate their performance, we conducted an experimental study based on both simulated data and real ECG signals from the MIT-BIH arrhythmia database. In general, the obtained results show that the proposed strategies exhibit a promising capability to select samples that are significant for the classification process, i.e., to boost the accuracy of the classification process while minimizing the number of involved labeled samples.
Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy. pasolli@disi.unitn.it
Edoardo
E
Pasolli
Farid
F
Melgani
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D004562:Electrocardiography / Q000145:classification / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted