Automatic Prediction of Cardiovascular and Cerebrovascular Events Using Heart Rate Variability Analysis

There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an autom...

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Veröffentlicht in:	PloS one Jg. 10; H. 3; S. e0118504
Hauptverfasser:	Melillo, Paolo, Izzo, Raffaele, Orrico, Ada, Scala, Paolo, Attanasio, Marcella, Mirra, Marco, De Luca, Nicola, Pecchia, Leandro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 20.03.2015 Public Library of Science (PLoS)
Schlagworte:	Acute coronary syndromes Aged Algorithms Artificial neural networks Automation Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - diagnostic imaging Cardiovascular Diseases - physiopathology Cerebral infarction Cerebrovascular Disorders - physiopathology Cerebrovascular system Classifiers Clinical medicine Data mining Data processing Decision Trees Education Electrocardiography Entropy Female Health risks Heart attacks Heart Rate Humans Hypertension Male Mathematical models Myocardial infarction Neural networks Patients Pattern recognition Physiology Prediction models Risk analysis Risk factors ROC Curve Stroke Studies Ultrasonic imaging Ultrasonography Variability Italy
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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