The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Classification and Feature Extraction
Abstract
In this chapter, the proposed optimization algorithm, kinetic gas molecule optimization (KGMO), that is based on swarm behaviour of gas molecules is applied to train a feedforward neural network for classification of ECG signals. Five types of ECG signals are used in this work including normal, supraventricular, brunch bundle block, anterior myocardial infarction (Anterior MI), and interior myocardial infarction (Interior MI). The classification performance of the proposed KGMO neural network (KGMONN) was evaluated on the Physiobank database and compared against conventional algorithms.
Related Content
Genevieve Z. Steiner-Lim, Madilyn Coles, Kayla Jaye, Najwa-Joelle Metri, Ali S. Butt, Katerina Christofides, Jackson McPartland, Zainab Al-Modhefer, Diana Karamacoska, Ethan Russo, Tim Karl.
© 2023.
47 pages.
|
Mohd Kashif, Mohammad Waseem, Poornima D. Vijendra, Ashok Kumar Pandurangan.
© 2023.
28 pages.
|
Courtney R. Acker, Rana R. Zeine.
© 2023.
27 pages.
|
Mahesh Pattabhiramaiah, Shanthala Mallikarjunaiah.
© 2023.
16 pages.
|
Dhairavi Shah, Dhaara Shah, Yara Mohamed, Danna Rosas, Alyssa Moffitt, Theresa Hearn Haynes, Francis Cortes, Taunjah Bell Neasman, Phani kumar Kathari, Ana Villagran, Rana R. Zeine.
© 2023.
28 pages.
|
Mohammad Uzair, Hammad Qaiser, Muhammad Arshad, Aneesa Zafar, Shahid Bashir.
© 2023.
23 pages.
|
Akila Muthuramalingam, Ashok Kumar Pandurangan, Subhamoy Banerjee.
© 2023.
17 pages.
|
|
|