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

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.