[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Erguzel et al., 2015 - Google Patents

A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

Erguzel et al., 2015

Document ID
13736645149354499968
Author
Erguzel T
Ozekes S
Sayar G
Tan O
Tarhan N
Publication year
Publication venue
Neurocomputing

External Links

Snippet

Classification of psychiatric disorders is becoming one of the major focuses of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory outcomes using biological biomarkers. The use of …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-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
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-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
    • G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476—Electroencephalography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG
    • A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
    • A61B5/4064—Evaluating the brain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety

Similar Documents

Publication Publication Date Title
Ahmadlou et al. Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD
Sabeti et al. A new approach for EEG signal classification of schizophrenic and control participants
Erguzel et al. Artificial intelligence approach to classify unipolar and bipolar depressive disorders
Erguzel et al. A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder
Tikka et al. Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study
Spyrou et al. Geriatric depression symptoms coexisting with cognitive decline: a comparison of classification methodologies
Najafzadeh et al. Automatic classification of schizophrenia patients using resting-state EEG signals
Modir et al. A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer's disease
Gangwar et al. Classical and intelligent computing methods in psychiatry and neuropsychitry: an overview
Kouka et al. EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition
Noor et al. Improving outcome prediction for traumatic brain injury from imbalanced datasets using RUSBoosted trees on electroencephalography spectral power
García-Ponsoda et al. Feature engineering of EEG applied to mental disorders: a systematic mapping study
Erguzel et al. Machine learning approaches to predict repetitive transcranial magnetic stimulation treatment response in major depressive disorder
Aditya et al. Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data
Mahato et al. Analysis of region of interest (RoI) of brain for detection of depression using EEG signal
Chen Decoding pain from brain activity
Iram et al. A classifier fusion strategy to improve the early detection of neurodegenerative diseases
Subasi Disease Prediction Using Artificial Intelligence: A case study on epileptic seizure prediction
Cui et al. Multilayer-aggregation functional network for identifying brain fatigue and diseases
Balaji et al. Classifying subjects with pfc lesions from healthy controls during working memory encoding via graph convolutional networks
Erguzel et al. Author's Accepted Manuscript
Chiranjeevi et al. Identification of Epileptic Seizures using Recurrent Neural Networks and Time Series Transformer
Kumar et al. Convolutional Neural Network for Seizure Detection Using Scalp Electroencephalogram (EEG)
Grigorovsky et al. Machine intelligence-based epileptic seizure forecasting
Faraz et al. Classification of Normal and Alcoholic EEG Signals Using Signal Processing and Machine Learning