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

Hassan et al., 2024 - Google Patents

NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks.

Hassan et al., 2024

View PDF
Document ID
5523860844175670182
Author
Hassan M
Haque R
Shariful Islam S
Meshref H
Alroobaea R
Masud M
Bairagi A
Publication year
Publication venue
AIMS Bioengineering

External Links

Snippet

This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term …
Continue reading at www.aimspress.com (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
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/322Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • 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
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/0452Detecting specific parameters of the electrocardiograph cycle
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • 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
Gómez et al. Automatic seizure detection based on imaged-EEG signals through fully convolutional networks
Hartmann et al. Automatic a-phase detection of cyclic alternating patterns in sleep using dynamic temporal information
Lim et al. Analysis of single-electrode EEG rhythms using MATLAB to elicit correlation with cognitive stress
Khan et al. D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism
Zhang et al. A cascaded convolutional neural network for assessing signal quality of dynamic ECG
Paragliola et al. An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients
Soni et al. Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression
Li et al. Patient-specific seizure prediction from electroencephalogram signal via multichannel feedback capsule network
Ullah et al. An End‐to‐End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
Saini et al. An extensive review on development of EEG-based computer-aided diagnosis systems for epilepsy detection
Bouazizi et al. Enhancing accuracy and interpretability in EEG-based medical decision making using an explainable ensemble learning framework application for stroke prediction
Liu et al. A review of arrhythmia detection based on electrocardiogram with artificial intelligence
Aziz et al. Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns
Tago et al. Classification of TCM pulse diagnoses based on pulse and periodic features from personal health data
Hemakom et al. ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study
Bongiorni et al. Evaluation of recurrent neural networks as epileptic seizure predictor
Mortensen et al. Multi-class stress detection through heart rate variability: A deep neural network based study
Qiu et al. A novel EEG-based Parkinson’s disease detection model using multiscale convolutional prototype networks
Chanu et al. An automated epileptic seizure detection using optimized neural network from EEG signals
Al-hajjar et al. Epileptic seizure detection using feature importance and ML classifiers
Singh et al. Automated detection of mental disorders using physiological signals and machine learning: A systematic review and scientometric analysis
Hassan et al. NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks.
Ji et al. An effective fusion model for seizure prediction: GAMRNN
Satapathy et al. AutoSleepNet: a multi-signal framework for automated sleep stage classification
Radhakrishnan et al. A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG