NDLI: Supervised and unsupervised learning for diagnostic ECG classification

Content Provider	IEEE Xplore Digital Library
Author	Silipo, N. Bortolan, G. Marchesi, C.
Copyright Year	1996
Description	Author affiliation: Dept. of Syst. & Inf., Florence Univ., Italy (Silipo, N.)
Abstract	A hybrid system with RBF pre-processing, a system with supervised learning, is compared with some Kohonen self-organizing maps in a subtle ECG classification task. Based on ECG measures, they are supposed to detect normal condition, presence of infarction and of hypertrophy, and at the same time to sub-classify those pathologies. During the evaluation process the hybrid system produces better results. In terms of average sensitivity and specificity (83% vs. 62% of sensitivity and 84% vs. 92% of specificity), but Kohonen maps allow a detailed description of the similarities among input data. An integration of the two techniques should improve the final results.
Starting Page	931
Ending Page	932
File Size	388574
Page Count	2
File Format	PDF
ISBN	0780338111
DOI	10.1109/IEMBS.1996.652647
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-10-31
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Unsupervised learning Electrocardiography Self organizing feature maps Supervised learning Neural networks Signal analysis Engineering in Medicine and Biology Society Time measurement Pathology Sensitivity and specificity
Content Type	Text
Resource Type	Article

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Supervised and unsupervised learning for diagnostic ECG classification