Article
Biology
Amir Omidvarnia, Aaron E. L. Warren, Linda J. Dalic, Mangor Pedersen, Graeme Jackson
Summary: Automated detection of generalized paroxysmal fast activity (GPFA), a generalized IED seen in patients with Lennox-Gastaut syndrome (LGS), was successfully developed using time-frequency information derived from manually marked IEDs in scalp EEG recordings. EEG-fMRI analysis of automatically detected events showed comparable results to manual IED markup, demonstrating the validity of this approach. The proposed framework offers a fast, automated, and objective method for inspection of generalized IEDs in LGS and potentially other epilepsy syndromes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Engineering, Electrical & Electronic
Guofa Li, Long Zhang, Ying Zou, Delin Ouyang, Yufei Yuan, Qiuyan Lian, Wenbo Chu, Gang Guo
Summary: This article examines the potential of using EEG signals from only one frequency band or from only a small subset of related electrode channels in recognizing driver vigilance state. The experimental results show that the recognition accuracy is higher when using EEG signals from the selected frequency band (i.e., alpha band) or the selected electrodes (i.e., T7, TP7, and CP1) than when using all the data. These results indicate that higher driver vigilance recognition accuracy can be achieved with much less amount of data, which would facilitate the development of wearable equipment based on EEG signals.
IEEE SENSORS JOURNAL
(2023)
Article
Clinical Neurology
Catherine Kulick-Soper, Russell T. Shinohara, Colin A. Ellis, Taneeta M. Ganguly, Ramya Raghupathi, Jay S. Pathmanathan, Erin C. Conrad
Summary: This study aimed to determine the effectiveness of pharmacologic paralysis and quantitative artifact reduction in improving the detection of epileptiform discharges. The results showed that paralysis significantly improved the detection rate, while artifact reduction was effective in cases with high EMG artifact but not with myoclonus artifact.
CLINICAL NEUROPHYSIOLOGY
(2023)
Article
Clinical Neurology
Maurice Abou Jaoude, Claire S. Jacobs, Rani A. Sarkis, Jin Jing, Kyle R. Pellerin, Andrew J. Cole, Sydney S. Cash, M. Brandon Westover, Alice D. Lam
Summary: This diagnostic study aimed to develop and validate a machine learning algorithm that accurately detects hippocampal epileptiform activity (HEA) from a standard scalp EEG, providing a novel, noninvasive, quantitative, and clinically relevant biomarker of hippocampal hyperexcitability in humans.
Article
Clinical Neurology
Christian Sandoe Musaeus, Troels Wesenberg Kjaer, Melita Cacic Hribljan, Birgitte Bo Andersen, Peter Hogh, Preben Kidmose, Martin Fabricius, Martin Christian Hemmsen, Mike Lind Rank, Gunhild Waldemar, Kristian Steen Frederiksen
Summary: Patients with DLB have a higher occurrence of interictal epileptiform discharges (IED) compared to healthy controls. These findings suggest that IED may be a consequence of neurodegeneration in patients with DLB.
MOVEMENT DISORDERS
(2023)
Article
Chemistry, Multidisciplinary
Maritza Mera-Gaona, Diego M. Lopez, Rubiel Vargas-Canas
Summary: The study utilized an ensemble feature selection method to improve the precision of distinguishing normal and abnormal EEG signals, demonstrating the stability and effectiveness of the approach. Evaluation results indicated that classifiers trained with the EFS features showed enhanced performance and achieved high accuracy, sensitivity, and specificity.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Biomedical
Fabrizio Vecchio, Lorenzo Nucci, Chiara Pappalettera, Francesca Miraglia, Daniela Iacoviello, Paolo Maria Rossini
Summary: This study investigates the differences between directional and non-directional visual stimuli processing by analyzing brain electroencephalographic activity. The results show that the processing of visual stimuli enhances similar patterns of spectral modulation in different brain regions, with significant differences observed in the occipital region. These findings suggest that event related spectral perturbations (ERSPs) could be a useful tool for studying visual information encoding in the brain.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Neurosciences
Christian Sandoe Musaeus, Kristian Steen Frederiksen, Birgitte Bo Andersen, Peter Hogh, Preben Kidmose, Martin Fabricius, Melita Cacic Hribljan, Martin Christian Hemmsen, Mike Lind Rank, Gunhild Waldemar, Troels Wesenberg Kjaer
Summary: In this study, it was found that most patients with Alzheimer's disease (AD) had epileptiform discharges, with a three-fold higher frequency compared to healthy elderly controls (HC), which most likely originated from the temporal lobes. This result suggests that elevated spike frequency should be considered a marker of hyperexcitability in AD.
NEUROBIOLOGY OF DISEASE
(2023)
Article
Chemistry, Analytical
Vedran Jurdana, Miroslav Vrankic, Nikola Lopac, Guruprasad Madhale Jadav
Summary: In this paper, a novel method for automatic estimation of instantaneous frequency (IF) and group delay (GD) is proposed to detect seizures with both spike and oscillatory characteristics. The method combines IF estimation algorithms for multicomponent signals with information obtained from localized Renyi entropies (LREs) to improve signal ridge estimation in the time-frequency distribution (TFD). Experimental results show the superiority of the proposed method in comparison to IF estimation alone, without requiring prior knowledge about the input signal.
Article
Computer Science, Artificial Intelligence
Chenchen Cheng, Yuanfeng Zhou, Bo You, Yan Liu, Gao Fei, Liling Yang, Yakang Dai
Summary: This study developed a novel multiview feature fusion representation (MVFFR) method to detect EEG signals with/without interictal epileptiform spikes (IES). The experimental results showed that MVFFR achieved the optimal detection performance compared with other feature ranking methods, and the MVFFR-related methods were complementary and indispensable. Additionally, MVFFR maintained excellent generalization capacity in an independent test.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Clinical Neurology
Mengrong Miao, Yaqian Han, Ying Zhang, Yuehua Xu, Liyuan Zhang, Yitian Yang, Mingyang Sun, Jiaqiang Zhang
Summary: The study found that the incidence of epileptiform EEG events in children during sevoflurane anesthesia varied from 19.1% to 59.2%. Using a low initial concentration of sevoflurane induction can reduce the incidence of these epileptiform discharges, while longer exposure to high concentration sevoflurane may increase the rate of epileptiform discharges.
CLINICAL NEUROPHYSIOLOGY
(2022)
Article
Acoustics
Gang Yu
Summary: This paper presents a high-resolution time-frequency analysis method for analyzing strongly non-stationary signals, overcoming the blurry features issue of conventional methods. By addressing the non-reassigned point problem in MSST, the proposed method improves energy concentration and demonstrates effectiveness in analyzing real-world signals.
JOURNAL OF SOUND AND VIBRATION
(2021)
Article
Mathematics, Interdisciplinary Applications
Tasmi Tamanna, Md Anisur Rahman, Samia Sultana, Mohammad Hasibul Haque, Mohammad Zavid Parvez
Summary: This paper aims to predict epileptic seizures in advance with high prediction accuracy from EEG signals using time-frequency feature extraction and classification techniques. The average prediction accuracy of the proposed method was observed to be 96.38%, and the method could predict a seizure 26.1 min before the actual occurrence. The findings suggest the potential future application of this method in seizure prediction.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Computer Science, Theory & Methods
Nabeel Ali Khan, Mokhtar Mohammadi, Mubeen Ghafoor, Syed Ali Tariq
Summary: In this paper, a new method based on convolutional neural network for enhancing time-frequency images is proposed, which trains CNN using specific input and output images. The experimental results show significant improvement compared to other state-of-the-art TF enhancement methods.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Biomedical
Jonathan Dan, Mette Thrane Foged, Benjamin Vandendriessche, Wim Van Paesschen, Alexander Bertrand
Summary: The objective of this paper is to investigate the limits of EEG sensor miniaturization in recording interictal epileptiform discharges in people with epilepsy. The researchers developed a computationally efficient sensor selection and interictal spike detection algorithm and found that EEG equipment should be designed to measure small signal power at short inter-electrode distances. They also found that a minimum inter-electrode distance of 5 cm between electrodes in a setup with at least two EEG units is needed for equivalent performance in interictal spike detection. These findings are significant for the design of miniaturized EEG systems for long-term monitoring of interictal spikes in epilepsy patients.
JOURNAL OF NEURAL ENGINEERING
(2023)