AIMC Topic: Scalp

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Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

Journal of healthcare engineering Mar 15, 2022
OBJECTIVE: To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.
Seizures Hemorrhage Scalp Electroencephalography Epilepsy Humans Deep Learning
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Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks.

PloS one Feb 28, 2022
We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with re...
Male Electroencephalography Neural Networks, Computer Female Scalp Epilepsy Seizures Humans
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Scalp EEG-Based Pain Detection Using Convolutional Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Feb 9, 2022
Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine lear...
Humans Scalp Electroencephalography Neural Networks, Computer Machine Learning Pain
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Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals.

IEEE journal of biomedical and health informatics Feb 4, 2022
Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interic...
Humans Electroencephalography Seizures Scalp Algorithms Deep Learning
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Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 31, 2022
Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephal...
Scalp Electroencephalography Electrodes Humans Neural Networks, Computer Depression
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Deep learning based smart health monitoring for automated prediction of epileptic seizures using spectral analysis of scalp EEG.

Physical and engineering sciences in medicine Sep 1, 2021
Being one of the most prevalent neurological disorders, epilepsy affects the lives of patients through the infrequent occurrence of spontaneous seizures. These seizures can result in serious injuries or unexpected deaths in individuals due to acciden...
Electroencephalography Seizures Epilepsy Quality of Life Scalp Humans Deep Learning Algorithms
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A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Journal of neural engineering Jul 21, 2021
Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time ...
Deep Learning Electroencephalography Epilepsy Scalp Humans
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Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

International journal of neural systems Jul 16, 2021
Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free ...
Scalp Electroencephalography Epilepsy Neural Networks, Computer Deep Learning Humans
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Deep learning for robust detection of interictal epileptiform discharges.

Journal of neural engineering Apr 8, 2021
Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs bas...
Electroencephalography Epilepsy Brain Deep Learning Humans Scalp
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Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts.

Lasers in surgery and medicine Sep 22, 2020
BACKGROUND AND OBJECTIVES: One of the challenges in developing effective hair loss therapies is the lack of reliable methods to monitor treatment response or alopecia progression. In this study, we propose the use of optical coherence tomography (OCT...
Hair Follicle Alopecia Tomography, Optical Coherence Scalp Hair Humans Deep Learning
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