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Detection and Classification of Epileptiform Transients in EEG signals using Convolution Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Detection and Classification of Epileptiform Transients in EEG signals using Convolution Neural Networks./
Author:	Ganta, Ashish.
Description:	1 online resource (110 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355344783

Detection and Classification of Epileptiform Transients in EEG signals using Convolution Neural Networks.
Ganta, Ashish.

Detection and Classification of Epileptiform Transients in EEG signals using Convolution Neural Networks. - 1 online resource (110 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--Clemson University, 2017.

Includes bibliographical references

EEG is the most common test done by neurologists to study a patient's brainwaves for pre-epileptic conditions. This thesis explains an end-to-end deep learning approach for detect- ing segments of EEG which display abnormal brain activity (Yellow-Boxes) and further classifying them to AEP (Abnormal Epileptiform Paroxysmals) and Non-AEP. It is treated as a binary and a multi-class problem. 1-D Convolution Neural Networks are used to carry out the identification and classification. Detection of Yellow-Boxes and subsequent analysis is a tedious process which can be fre- quently misinterpreted by neurologists without neurophysiology fellowship training. Hence, an automated machine learning system to detect and classify will greatly enhance the quality of diagnosis. Two convolution neural network architectures are trained for the detection of Yellow-Boxes as well as their classification. The first step is detecting the Yellow-Boxes. This is done by training convolution neural networks on a training set containing both Yellow-Boxed and Non-Yellow Boxed segments treated as a 2 class problem, and is also treated as a class extension to the classification of the Yellow-Boxes problem. The second step is the classification of the Yellow-Boxes, where 2 different architectures are trained to classify the Yellow-Boxed data to 2 and 4 classes. The over-all system is validated with the entire 30s EEG segments of multiple patients, which the system classifies as Yellow-Boxes or Non-Yellow Boxes and subsequent classification to AEP or Non-AEP, and is compared with the annotated data by neurologists.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355344783Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Detection and Classification of Epileptiform Transients in EEG signals using Convolution Neural Networks.
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500 $a Source: Masters Abstracts International, Volume: 57-02.
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502 $a Thesis (M.S.)--Clemson University, 2017.
504 $a Includes bibliographical references
520 $a EEG is the most common test done by neurologists to study a patient's brainwaves for pre-epileptic conditions. This thesis explains an end-to-end deep learning approach for detect- ing segments of EEG which display abnormal brain activity (Yellow-Boxes) and further classifying them to AEP (Abnormal Epileptiform Paroxysmals) and Non-AEP. It is treated as a binary and a multi-class problem. 1-D Convolution Neural Networks are used to carry out the identification and classification. Detection of Yellow-Boxes and subsequent analysis is a tedious process which can be fre- quently misinterpreted by neurologists without neurophysiology fellowship training. Hence, an automated machine learning system to detect and classify will greatly enhance the quality of diagnosis. Two convolution neural network architectures are trained for the detection of Yellow-Boxes as well as their classification. The first step is detecting the Yellow-Boxes. This is done by training convolution neural networks on a training set containing both Yellow-Boxed and Non-Yellow Boxed segments treated as a 2 class problem, and is also treated as a class extension to the classification of the Yellow-Boxes problem. The second step is the classification of the Yellow-Boxes, where 2 different architectures are trained to classify the Yellow-Boxed data to 2 and 4 classes. The over-all system is validated with the entire 30s EEG segments of multiple patients, which the system classifies as Yellow-Boxes or Non-Yellow Boxes and subsequent classification to AEP or Non-AEP, and is compared with the annotated data by neurologists.
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