A Supervised Machine Learning Algorithm for Arrhythmia AnalysisH

  title={A Supervised Machine Learning Algorithm for Arrhythmia AnalysisH},
  author={Altay G uvenir and Burak Açar},
A new machine learning algorithm for the diagno sis of cardiac arrhythmia from standard lead ECG recordings is presented The algorithm is called VFI for Voting Feature Intervals VFI is a supervised and inductive learning algorithm for inducing classi cation knowledge from examples The input to VFI is a train ing set of records Each record contains clinical mea surements from ECG signals and some other infor mation such as sex age and weight along with the decision of an expert cardiologist The… CONTINUE READING
Highly Cited
This paper has 133 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 69 extracted citations

Pattern Recognition

Axel Pinz Thomas Pock, Horst Bischof Franz Leberl
Lecture Notes in Computer Science • 2012
View 20 Excerpts
Highly Influenced

Beat discovery from dimensionality reduced perspective streams of electrocardiogram signal data

2015 12th International Joint Conference on e-Business and Telecommunications (ICETE) • 2015
View 4 Excerpts
Highly Influenced

Identifying best feature subset for cardiac arrhythmia classification

2015 Science and Information Conference (SAI) • 2015
View 4 Excerpts
Highly Influenced

133 Citations

Citations per Year
Semantic Scholar estimates that this publication has 133 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-4 of 4 references

Classi cation by Voting Feature Intervals

G. Demir oz, H A.Guvenir
Proceedings of 9th European Conference on Machine Learning. Prague: Springer- Verlag, • 1997
View 1 Excerpt

Classi cation by Feature Par- titioning

H A.Guvenir, I. S irin
Machine Learning • 1996
View 1 Excerpt

Inductive and Bayesian learning in medical diagnosis

Applied Artificial Intelligence • 1993
View 1 Excerpt

Similar Papers

Loading similar papers…