M Asjid Tanveer1, Muhammad Jawad Khan2, Hasan Sajid3, Noman Naseer4. 1. Intelligent Robotics Lab, National Center of Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan. 2. Intelligent Robotics Lab, National Center of Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan; School of Mechanical and Manufacturing Engineering, National Center of Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan. Electronic address: jawad.khan@smme.nust.edu.pk. 3. Intelligent Robotics Lab, National Center of Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan; School of Mechanical and Manufacturing Engineering, National Center of Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan. 4. Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan.
Abstract
BACKGROUND: Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states. NEW METHOD: In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states. RESULTS: The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively. COMPARISON WITH EXISTING METHODS: This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation. CONCLUSION: The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
BACKGROUND: Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states. NEW METHOD: In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states. RESULTS: The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively. COMPARISON WITH EXISTING METHODS: This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation. CONCLUSION: The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
Authors: Ana Borovac; Steinn Gudmundsson; Gardar Thorvardsson; Saeed M Moghadam; Paivi Nevalainen; Nathan Stevenson; Sampsa Vanhatalo; Thomas P Runarsson Journal: IEEE J Transl Eng Health Med Date: 2022-08-23