Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128132
Citations
Scopus Web of Science® Altmetric
?
?
Type: Conference paper
Title: Improved A-phase detection of cyclic alternating pattern using deep learning
Author: Hartmann, S.
Baumert, M.
Citation: Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019), 2019 / vol.2019, pp.1-4
Publisher: IEEE
Issue Date: 2019
Series/Report no.: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
ISBN: 1538613123
9781538613122
ISSN: 1557-170X
1558-4615
Conference Name: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (23 Jul 2019 - 27 Jul 2019 : Berlin, Germany)
Statement of
Responsibility: 
Simon Hartmann and Mathias Baumert
Abstract: In recent years, machine learning algorithms have become increasingly popular for analyzing biomedical signals. This includes the detection of cyclic alternating pattern (CAP) in electroencephalography recordings. Here, we investigate the performance gain of a recurrent neural network (RNN) for CAP scoring in comparison to standard classification methods. We analyzed 15 recordings (n1-n15) from the publicly available CAP Sleep Database on Physionet to evaluate each machine learning method. A long short-term memory (LSTM) network increases the accuracy and F1-score by 0.5-3.5% and 3.5-8%, respectively, compared to commonly used classification algorithms such as linear discriminant analysis, k-nearest neighbour or feed-forward neural network. Our results show that by using a LSTM classifier the quantity of correctly detected CAP events can be increased and the number of wrongly classified periods reduced. RNNs significantly improve the precision in CAP scoring by taking advantage of available information from the past for deciding current classification.
Keywords: Humans; Electroencephalography; Algorithms; Deep Learning; Neural Networks, Computer
Rights: ©2019 IEEE
RMID: 1000003218
DOI: 10.1109/EMBC.2019.8857006
Published version: https://ieeexplore.ieee.org/xpl/conhome/8844528/proceeding
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.