Title :
Using machine learning to detect problems in ECG data collection
Author :
Kalkstein, Nir ; Kinar, Yaron ; Aman, Michael Na ; Neumark, Nir ; Akiva, Pini
Author_Institution :
Medial Res., Ramot-Hashavim, Israel
Abstract :
We describe a data-driven approach, using a combination of machine learning algorithms to solve the 2011 Physionet/Computing in Cardiology (CinC) challenge - identifying data collection problems at 12 leads electrocardiography (ECG). Our data-driven approach reaches an internal (cross-validation) accuracy of almost 93% on the training set, and accuracy of 91.2% on the test set.
Keywords :
electrocardiography; learning (artificial intelligence); medical signal processing; ECG data collection; data collection problem identification; data-driven approach; electrocardiography; machine learning; Accuracy; Electrocardiography; Lead; Machine learning; Medical services; Training; Vegetation;
Conference_Titel :
Computing in Cardiology, 2011
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4577-0612-7
