Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review
Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in ...
Source: Clinical EEG and Neuroscience - October 4, 2023 Category: Neuroscience Authors: Francesco Amico Jaroslaw Lucas Koberda Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG
In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. ...
Source: Clinical EEG and Neuroscience - September 8, 2023 Category: Neuroscience Authors: Nupur Chugh Swati Aggarwal Arnav Balyan Source Type: research
