Article
Chemistry, Multidisciplinary
Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta
Summary: This paper presents an automatic technique using dual-tree complex wavelet transform (DTCWT) to extract features from epileptic seizures' EEG signals and classify them. The proposed technique achieved excellent results in seizure-wise and patient-wise classification, setting new benchmark results in this field.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Sidra Naseem, Kashif Javed, Muhammad Jawad Khan, Saddaf Rubab, Muhammad Attique Khan, Yunyoung Nam
Summary: Electroencephalography is commonly used to record brain signals, but manual analysis of EEGs is complicated and time-consuming. Deep learning models, including CNN, are introduced as a solution to classify EEG signals efficiently.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Biochemistry & Molecular Biology
Ramy Hussein, Soojin Lee, Rabab Ward
Summary: In this study, a Transformer-based approach called MViT is introduced for automated learning of spatio-temporal-spectral features in multi-channel EEG data. Extensive experiments demonstrate the superiority of MViT algorithm in seizure prediction.
Article
Computer Science, Artificial Intelligence
Ramy Hussein, Soojin Lee, Rabab Ward, Martin J. McKeown
Summary: This study introduces a novel semi-dilated convolutional neural network architecture that outperforms previous methods in predicting epileptic seizures, achieving an average prediction sensitivity of 98.90% for scalp EEG.
Article
Mathematics, Interdisciplinary Applications
Arshpreet Kaur, Kumar Shashvat
Summary: This study aims to automate the identification of inter-ictal activity from EEG and distinguish it from the activity of a controlled patient. The researchers used the Bonn dataset and patient data collected from Max Hospital, Saket, and applied Continuous Wavelet Transform and a fifteen-layer Convolutional Neural Network for signal classification. The results show that the proposed method outperforms existing methods in terms of performance metrics, and scalograms are effective for identifying epileptic states.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Artificial Intelligence
Ibrahim Aliyu, Chang Gyoon Lim
Summary: This paper proposes an LSTM network for classifying epileptic EEG signals. Discrete wavelet transform is used to remove noise and extract features, and the optimal features are identified through correlation and P value analysis. The proposed method achieves high accuracy and outperforms other popular classifiers.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Biomedical
Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta
Summary: In this paper, a novel multi-path seizure-type classification deep learning network (MP-SeizNet) is proposed, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism, to classify specific types of seizures using electroencephalogram (EEG) data. The proposed MP-SeizNet is fed with two different representations of EEG data, wavelet-based features extracted from the EEG signals for CNN and raw EEG signals for Bi-LSTM, allowing the model to jointly learn from different representations for more accurate information learning. The evaluation of MP-SeizNet using the Temple University Hospital EEG Seizure Corpus achieves F1-scores of 87.6% and 98.1% in three-fold cross-validation for different patient data and five-fold cross-validation for seizure data, respectively.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Matteo Dora, David Holcman
Summary: This paper proposes a new wavelet-based method for removing artifacts from single-channel EEGs. The method adaptively attenuates artifacts of different nature through data-driven renormalization of wavelet components and demonstrates superior performances on different kinds of artifacts and signal-to-noise levels. The proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Biomedical
Kalyani P. Wagh, K. Vasanth
Summary: This paper presents an approach for emotion recognition using EEG signals, with experiments conducted using the SEED database. Various features and classifiers are utilized for emotion state classification. The trial results show that decision tree and K nearest neighbor methods perform well in emotion recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Engineering, Biomedical
P. P. Mini, Tessamma Thomas, R. Gopikakumari
Summary: The research introduces an Automatic Speech Recognition system based on audio and EEG signals, demonstrating that combining different modalities can enhance speech recognition accuracy. The results indicate the possibility of speech recognition from EEG signals and the potential for improving recognition rate by fusing audio with EEG.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Biomedical
Qi Xin, Shaohai Hu, Shuaiqi Liu, Ling Zhao, Yu-Dong Zhang
Summary: This paper proposes an Attention Mechanism-based Wavelet Convolution Neural Network for epilepsy EEG classification, achieving high accuracy through multi-scale wavelet analysis and attention mechanism.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Computer Science, Information Systems
Varsha Harpale, Vinayak Bairagi
Summary: EEG analysis plays a crucial role in detecting and predicting various brain diseases, with a focus on classifying normal EEG signals from epileptic EEG signals. The study aims to identify pre-seizure and seizure states of EEG signals using time and frequency features, utilizing a fuzzy classifier for prediction accuracy.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Hong He, Xinyue Liu, Yong Hao
Summary: The PDWC, based on DWT and RF, mimics the progressive object identification process of human beings with recognition cycles to enhance wavelet energy features and improve recognition accuracy through cascade RF classifiers. It outperforms traditional schemes and deep learning methods with a mean accuracy of 0.9914 for diverse EEG signals.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Veterinary Sciences
Julia Luca, Samantha McCarthy, Thomas Parmentier, Michal Hazenfratz, Alex Zur Linden, Luis Gaitero, Fiona M. K. James
Summary: This survey study aims to understand the current usage, techniques, and barriers of canine EEG in veterinary neurology. The results show that less than 50% of veterinary neurologists are currently using EEG and it is performed infrequently. The main barriers to performing EEG in dogs are equipment availability, insufficient cases, and financial costs to clients.
FRONTIERS IN VETERINARY SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ranjit Kumar Paul, Sandip Garai
Summary: Accurate forecasting in Indian agriculture is crucial, with machine learning techniques like artificial neural network and wavelet transformation being effective in handling nonlinear datasets to improve model accuracy.
Article
Health Care Sciences & Services
Adem Goluck, Hakan Isik, Inan Guler
JOURNAL OF MEDICAL SYSTEMS
(2016)
Article
Biology
Hakan Isik, Evren Sezgin, Mustafa Cihat Avunduk
COMPUTERS IN BIOLOGY AND MEDICINE
(2010)
Article
Computer Science, Artificial Intelligence
Hakan Isik, Sema Arslan
EXPERT SYSTEMS WITH APPLICATIONS
(2011)
Article
Computer Science, Artificial Intelligence
Novruz Allahverdi, Ayfer Tunali, Hakan Isik, Humar Kahramanli
EXPERT SYSTEMS WITH APPLICATIONS
(2011)
Article
Health Care Sciences & Services
Hakan Isik, Esra Saracoglu, Hueseyin Harmanci, Inan Guler
JOURNAL OF MEDICAL SYSTEMS
(2010)
Article
Health Care Sciences & Services
Hakan Isik, Esma Sezer
JOURNAL OF MEDICAL SYSTEMS
(2012)
Article
Health Care Sciences & Services
Hakan Isik, Sema Arslan
JOURNAL OF MEDICAL SYSTEMS
(2011)
Article
Computer Science, Software Engineering
Esra Satir, Hakan Isik
JOURNAL OF SYSTEMS AND SOFTWARE
(2012)
Article
Computer Science, Information Systems
Esra Satir, Hakan Isik
MULTIMEDIA TOOLS AND APPLICATIONS
(2014)
Proceedings Paper
Engineering, Mechanical
Hasan Aydogan, A. Engin Ozcelik, Mustafa Acaroglu, Hakan Isik
ADVANCED RESEARCH IN MATERIAL SCIENCE AND MECHANICAL ENGINEERING, PTS 1 AND 2
(2014)