AIMC Topic: Electroencephalography

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Depression recognition using machine learning methods with different feature generation strategies.

Artificial intelligence in medicine Jul 17, 2019
The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method...
Depression Humans Emotions Neural Networks, Computer Electroencephalography Machine Learning Sensitivity and Specificity Deep Learning China Signal Processing, Computer-Assisted Face Diagnostic and Statistical Manual of Mental Disorders
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Correlation-based channel selection and regularized feature optimization for MI-based BCI.

Neural networks : the official journal of the International Neural Network Society Jul 15, 2019
Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI perfor...
Humans Brain-Computer Interfaces Support Vector Machine Electroencephalography
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A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram.

Journal of neuroscience methods Jul 13, 2019
BACKGROUND: Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data...
Signal Processing, Computer-Assisted Cerebral Cortex Humans Machine Learning Adult Support Vector Machine Electroencephalography Epilepsy
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The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.

Clinical EEG and neuroscience Jul 11, 2019
. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with a...
Early Diagnosis Male Adolescent Machine Learning Child Child, Preschool Autism Spectrum Disorder Humans Diagnosis, Differential Female Neural Networks, Computer Electroencephalography
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Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals.

Computers in biology and medicine Jul 10, 2019
There have been different efforts to predict epileptic seizures and most of them are based on the analysis of electroencephalography (EEG) signals; however, recent publications have suggested that functional Near-Infrared Spectroscopy (fNIRS), a rela...
Electroencephalography Seizures Signal Processing, Computer-Assisted Algorithms Spectroscopy, Near-Infrared Predictive Value of Tests Humans Neural Networks, Computer
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BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity.

IEEE transactions on biomedical circuits and systems Jul 9, 2019
Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. N...
Female Adult Male Humans Machine Learning Signal Processing, Computer-Assisted Middle Aged Brain-Computer Interfaces Wearable Electronic Devices Evoked Potentials, Visual Electroencephalography
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Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System.

Sensors (Basel, Switzerland) Jul 9, 2019
According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutr...
Humans Electroencephalography Neural Networks, Computer Electromyography Algorithms Multimedia Body Temperature Monitoring, Physiologic Deep Learning Wearable Electronic Devices Blood Pressure Electrocardiography Bayes Theorem
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Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

EBioMedicine Jul 9, 2019
BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, pri...
Neural Networks, Computer Precision Medicine Female Humans Electroencephalography Epilepsy Seizures Male Deep Learning Electrocardiography Electrocorticography
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Automated spectrographic seizure detection using convolutional neural networks.

Seizure Jul 8, 2019
PURPOSE: Non-convulsive seizures are common in critically ill patients, and delays in diagnosis contribute to increased morbidity and mortality. Many intensive care units employ continuous EEG (cEEG) for seizure monitoring. Although cEEG is continuou...
Seizures Critical Care Image Interpretation, Computer-Assisted Retrospective Studies Sensitivity and Specificity Electroencephalography Humans Neural Networks, Computer
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Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals.

BioMed research international Jul 7, 2019
Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) f...
Electroencephalography Female Humans Male Epilepsy Databases, Factual Support Vector Machine Signal Processing, Computer-Assisted
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