Davoodi et al., 2026 - Google Patents

Graph-Based EEG Symmetry Features from the Temporal Lobe as Markers of Antidepressant Treatment Response

Davoodi et al., 2026

View PDF
Document ID
501790445870553283
Author
Davoodi A
Vlachos I
Bareš M
Brunovský M
Paluš M
Publication year
Publication venue
bioRxiv

External Links

Snippet

Major Depressive Disorder (MDD) is a mental disorder that affects millions globally and has highly individualized responses to antidepressant treatment. Identifying objective early markers that can distinguish responders from non-responders remains a critical challenge in …
Continue reading at www.biorxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3437Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476Electroencephalography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/40Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/16Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • A61B5/165Evaluating the state of mind, e.g. depression, anxiety

Similar Documents

Publication Publication Date Title
Erguzel et al. A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders
Karthick et al. Prediction of secondary generalization from a focal onset seizure in intracerebral EEG
Kardam et al. Motor imagery tasks based electroencephalogram signals classification using data-driven features
Daftari et al. Detection of epileptic seizure disorder using EEG signals
da Silva Lourenço et al. Ultrafast review of ambulatory EEGs with deep learning
Kim et al. Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning
Yang et al. Learning optimal biomarker‐guided treatment policy for chronic disorders
Davoodi et al. Graph-Based EEG Symmetry Features from the Temporal Lobe as Markers of Antidepressant Treatment Response
Mannino et al. Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs
Ghassemi Life after death: techniques for the prognostication of coma outcomes after cardiac arrest
Alvarado-Rojas et al. Artificial intelligence applied to electroencephalography in epilepsy
Afzal et al. AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image-and Time-Series-Based Approaches
Wong et al. CRCC: Contrast-Based Robust Cross-Subject and Cross-Site Representation Learning for EEG
Packiyanathan et al. Towards a dream decoder: Classification and analysis of dream experiences during sleep using electroencephalography
Hazra et al. Bridging Accuracy and Explainability in EEG-based Graph Attention Network for Depression Detection
Lee et al. Predicting antiseizure medication response in newly diagnosed epilepsy using quantitative EEG and machine learning
Hailemicael Nasser et al. Learning Neural Patterns: Kolmogorov-Arnold Network Powered Classification of Schizophrenia in Electroencephalogram Data
da Silva Lourenço Deep Learning for EEG Analysis
Hung et al. EEG-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy
Balaji Computational Approaches to Epilepsy: Graph Networks for Localization and Multi-Model Approach for Prediction
Batista On the Clinical Acceptance of EEG Seizure Prediction Methodologies
Huang et al. Neural characteristics of emotion regulation and derived machine learning classification in high negative affectivity: Based on event-related potentials and nonlinear analysis
Gomes Automatic Error Detection and Prediction Based on Neuronal Signals
Olivotto Data-Driven Seizure Prediction Using EEG and ECG Signals
Joshi et al. EEG-Based Investigation of Attention Deficit/Hyperactivity Disorder (ADHD) Using Machine Learning