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Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks
journal contribution
posted on 2017-08-02, 00:00 authored by M Seera, Chee Peng LimChee Peng Lim, K S Tan, W S LiewTranscranial 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.
History
Journal
NeurocomputingVolume
249Pagination
337 - 344Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0925-2312eISSN
1872-8286Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2017, ElsevierUsage metrics
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