Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models
作者：
Highlights：
• Medical data classification problems with two real data sets are investigated.
• A literature review on biomedical signal processing techniques is provided.
• The data sets are corrupted with noise to assess the robustness of different models.
• The logistic regression model produces the best results in noise-free environments.
• Ensemble-based learning model yields the best results in noisy environments.
摘要
•Medical data classification problems with two real data sets are investigated.•A literature review on biomedical signal processing techniques is provided.•The data sets are corrupted with noise to assess the robustness of different models.•The logistic regression model produces the best results in noise-free environments.•Ensemble-based learning model yields the best results in noisy environments.
论文关键词：Machine learning,Data classification,Medical signals,Electrocardiogram,Auscultatory blood pressure
论文评审过程：Available online 20 December 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.12.023
