TY - JOUR
T1 - SASDL and RBATQ
T2 - Sparse Autoencoder with Swarm based Deep Learning and Reinforcement based Q-learning for EEG Classification
AU - Prabhakar, Sunil KUMAR
AU - Lee, Seong Whan
N1 - Publisher Copyright:
Author
PY - 2022
Y1 - 2022
N2 - The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc can be investigated well with the help of EEG signals. In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique. Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets.
AB - The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc can be investigated well with the help of EEG signals. In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique. Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets.
KW - Analytical models
KW - Brain modeling
KW - Deep Learning
KW - Deep learning
KW - EEG
KW - Electroencephalography
KW - Epilepsy
KW - PSO
KW - Q-learning
KW - Q-learning
KW - Reinforcement learning
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85127019017&partnerID=8YFLogxK
U2 - 10.1109/OJEMB.2022.3161837
DO - 10.1109/OJEMB.2022.3161837
M3 - Article
AN - SCOPUS:85127019017
JO - IEEE Open Journal of Engineering in Medicine and Biology
JF - IEEE Open Journal of Engineering in Medicine and Biology
SN - 2644-1276
ER -