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Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks

Seera, Manjeevan, Lim, Chee Peng, Tan, Kay Sin and Liew, Wei Shiung 2017, Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks, Neurocomputing, vol. 249, pp. 337-344, doi: 10.1016/j.neucom.2016.05.117.

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Title Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks
Author(s) Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Tan, Kay Sin
Liew, Wei Shiung
Journal name Neurocomputing
Volume number 249
Start page 337
End page 344
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-08-02
ISSN 0925-2312
1872-8286
Keyword(s) Doppler signals
Recurrent neural network
Pattern classification
Stenosis
Summary Transcranial Doppler (TCD) is a reliable technique with the advantage of being non-invasive for the diagnosis of cerebrovascular diseases using blood flow velocity measurements pertaining to the cerebral arterial segments. In this study, the recurrent neural network (RNN) is used to classify TCD signals captured from the brain. A total of 35 real, anonymous patient records are collected, and a series of experiments for stenosis diagnosis is conducted. The extracted features from the TCD signals are used for classification using a number of RNN models with recurrent feedbacks. In addition to individual RNN results, an ensemble RNN model is formed in which the majority voting method is used to combine the individual RNN predictions into an integrated prediction. The results, which include the accuracy, sensitivity, and specificity rates as well as the area under the Receiver Operating Characteristic curve, are compared with those from the Random Forest Ensemble model. The outcome positively indicates the usefulness of the RNN ensemble as an effective method for detecting and classifying blood flow velocity changes due to brain diseases.
Language eng
DOI 10.1016/j.neucom.2016.05.117
Field of Research 080101 Adaptive Agents and Intelligent Robotics
08 Information And Computing Sciences
09 Engineering
17 Psychology And Cognitive Sciences
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30095962

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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