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Raffone, Antonino ; Giorgi, Andrea ; Kwok, Hoi Fei
2005, nr 4
artykuł
The learning vector quantization (LVQ) network was used to classify the ECG ST segment into different morphological categories. Due to the lack of data in the ST elevation categories, the classifier was only trained to identify different types of ST depressions (horizontal, upsloping and downsloping). The accuracies were 91%, 85% and 65% respectively for the training, validation and testing data respectively. Despite the low accuracy for the testing data, most of the mis-classifications were downsloping ST depression being classified as horizontal ST depression. We concluded that more data and more training are needed in order to train the LVQ to recognize other morphological types of ST deviation and to improve the accuracy.
Instytut Łączności - Państwowy Instytut Badawczy, Warszawa
application/pdf
oai:bc.itl.waw.pl:502
10.26636/jtit.2005.4.336
1509-4553
1899-8852
Journal of Telecommunications and Information Technology
ang
Biblioteka Naukowa Instytutu Łączności
19 cze 2024
9 mar 2010
329
https://ribes-54.man.poznan.pl/publication/561
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OAI-PMH
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