Deep Learning for ECG Classification; Journal of Physics: Conference Series; Vol. 913 : BigData Conference (Formerly International Conference on Big Data and Its Applications)

Deep Learning for ECG Classification; Journal of Physics: Conference Series; Vol. 913 : BigData Conference (Formerly International Conference on Big Data and Its Applications)

Détails bibliographiques
Parent link:Journal of Physics: Conference Series
Vol. 913 : BigData Conference (Formerly International Conference on Big Data and Its Applications).— 2017.— [012004, 6 p.]
Auteur principal: Pyakullya B. I. Boris Ivanovich
Collectivité auteur: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение автоматизации и робототехники (ОАР)
Autres auteurs: Kazachenko N. E. Nataljya Evgenjevna, Mikhaylovskiy N. E. Nikolay Ernestovich
Résumé:Title screen
The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.
Langue:anglais
Publié: 2017
Sujets:
электронный ресурс
труды учёных ТПУ
ЭКГ
обучение
классификации
Accès en ligne:https://doi.org/10.1088/1742-6596/913/1/012004
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657793

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