Classification of coma etiology using convolutional neural networks and long-short term memory networks

Coma can be caused by different health conditions. Sometimes, patients are admitted to intensive care unit (ICU) without the cause of the coma being known. Knowing the coma etiology of a patient is very important for prognosis and treatment. Classification of electroencephalogram (EEG) signals by de...

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Bibliographic Details
Main Authors: Junior, Sergio Baldo, Carneiro, Murillo G., Destro-Filho, Joao-Batista, Zhao, Liang, Tins, Renato
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
CNN
Coma
Convolutional neural networks
Deep learning
EEG
Electroencephalogram
Electroencephalography
Etiology
Feature extraction
LSTM
Medical services
Predictive models
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Summary:Coma can be caused by different health conditions. Sometimes, patients are admitted to intensive care unit (ICU) without the cause of the coma being known. Knowing the coma etiology of a patient is very important for prognosis and treatment. Classification of electroencephalogram (EEG) signals by deep learning is proposed to help predict the coma etiology of ICU patients. EEG is a cheap noninvasive technique that can be used for the diagnostics and evaluation of neurological diseases. The objective is to classify coma etiology into one of four categories: Traumatic Brain Injury (TBI), Metabolic Coma, Stroke, and Other. A deep learning model based on convolutional neural networks (CNNs) is proposed to classify the EEG signals, using information from two different sources: i) intermediate layers of CNN + long-short term memory network (LSTM); ii) additional features from patients and statistical measures extracted from EEG signals. Outputs of the LSTM and additional features are inserted as additional inputs to the first dense layer of the CNN. The proposed model was compared to six other approaches, some of which incorporated additional features from patients or statistical measures from EEG signals, while others did not. Experimental results show that inserting patient information, like age and genre, as input to the first dense layer improve the predictive performance of the classification model. Moreover, this work suggests new possibilities to assist physicians in the detection of the coma etiology, especially those in small and far health units.
ISSN:2161-4407
DOI:10.1109/IJCNN54540.2023.10191203