Title :
Identifying 3-vessel and main stem disease during pain at rest using self-learning techniques
Author :
Dassen, Willem RM ; Gorgels, Anton PM ; Mulleneers, Rob GA ; Karthaus, Vincent LJ ; Van Els, Hugo ; Talmon, Jan L. ; Wellens, Hein JJ
Author_Institution :
Dept. of Cardiology & Med. Inf., Limburg Univ., Maastricht, Netherlands
Abstract :
Recently an electrocardiographic sign has been described enabling the recognition of 3-vessel or left main stem disease. In this study, using two self-learning techniques, the neural network and the induction algorithm approach, this sign was validated and further refined. Based on 113 ECGs, (63 training and 50 for testing), the influence of the number of parameters and the effect of additional weight factors to direct the classification process, was evaluated.<>
Keywords :
electrocardiography; medical signal processing; unsupervised learning; 3-vessel disease; classification process direction; electrocardiographic sign; induction algorithm approach; main stem disease; neural network technique; pain at rest; parameters number; self-learning techniques; weight factors; Biomedical informatics; Cardiac disease; Cardiology; Cardiovascular diseases; Decision trees; Electrocardiography; Neural networks; Neurons; Pain; Testing;
Conference_Titel :
Computers in Cardiology 1994
Conference_Location :
Bethesda, MD, USA
Print_ISBN :
0-8186-6570-X
DOI :
10.1109/CIC.1994.470136
