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. Attached Files Name Description MIMEType Size Downloads Title Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks Author(s) Seera, Manjeevan 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 Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 11 May 2017, 15:57:59 EST Fri, 12 May 2017, 05:09:44 EST Mon, 05 Jun 2017, 16:55:59 EST Tue, 06 Jun 2017, 06:05:34 EST Wed, 07 Jun 2017, 12:06:49 EST Wed, 07 Jun 2017, 12:09:09 EST Wed, 07 Jun 2017, 12:10:29 EST Sat, 22 Jan 2022, 01:31:29 EST Filtered Full Citation counts:   Cited 15 times in TR Web of Science Cited 18 times in Scopus Search Google Scholar Access Statistics: 364 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 05 Jun 2017, 16:55:59 EST