Item Description
Seller assumes all responsibility for this listing..
Listing: #4228771
- Condition: Used - Excellent
- Brand: CAREFUSION
- Type: Neurology General
- Model: SONARA Transcranial Doppler System TCD
3 Units available
CareFusion SONARA Transcranial Doppler System TCD Powers On.
No Problems Found. , In Great Cosmetic Condition.
Software Version 7.0
Manufactured in 2015 , 2013 , 2013
Each Unit Include 2MHz Doppler Monitoring Probe, Advance Hand Control with Key Pad
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.
CareFusion SONARA Transcranial Doppler System TCD Powers On.
No Problems Found. , In Great Cosmetic Condition.
Software Version 7.0
Manufactured in 2015 , 2013 , 2013
Each Unit Include 2MHz Doppler Monitoring Probe, Advance Hand Control with Key Pad
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.
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