Using Machine Learning to Detect Problems in ECG Data Collection

被引：0
|
作者：
Kalkstein, Nir [1 ]
Kinar, Yaron [1 ]
Na'aman, Michael [1 ]
Neumark, Nir [1 ]
Akiva, Pini [1 ]
机构：
[1] Medial Res, IL-45930 Ramot Hashavim, Israel
来源：
2011 COMPUTING IN CARDIOLOGY | 2011年 / 38卷
关键词：
D O I：
暂无
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
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.
引用
收藏
页码：437 / 440
页数：4
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