[go: up one dir, main page]

Hou et al., 2020 - Google Patents

Epilepsy detection using random forest classification based on locally linear embedding algorithm

Hou et al., 2020

Document ID
4298374156832185505
Author
Hou Q
Liu Y
Liu J
Sun S
Publication year
Publication venue
2020 5th International Conference on Control, Robotics and Cybernetics (CRC)

External Links

Snippet

Epilepsy is a common disease of the brain nervous system. The key to epilepsy surgery is to locate the epileptic foci. Research shows that they can be detected by magnetoencephalographic (MEG) data. The Random Forest Classification model based on …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • 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
    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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
    • 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
    • G06K9/6279Classification techniques relating to the number of classes
    • 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
    • A61B5/40Detecting, 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
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • 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
    • 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
    • A61B5/0488Electromyography
    • 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
    • A61B5/04012Analysis of electro-cardiograms, electro-encephalograms, electro-myograms

Similar Documents

Publication Publication Date Title
CN109645990B (en) A Computer Pattern Recognition Method for EEG Signals of Epilepsy Patients
Kumar et al. OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals
Aly et al. Bio-signal based motion control system using deep learning models: a deep learning approach for motion classification using EEG and EMG signal fusion
CN117503057B (en) Epileptic seizure detection device and medium for constructing brain network based on high-order tensor decomposition
Busia et al. EEGformer: Transformer-based epilepsy detection on raw EEG traces for low-channel-count wearable continuous monitoring devices
Hou et al. Epilepsy detection using random forest classification based on locally linear embedding algorithm
CN120130931A (en) Epileptic seizure detection method and system based on self-attention mechanism and GRU-LSTM fusion
Salafian et al. MICAL: Mutual information-based CNN-aided learned factor graphs for seizure detection from EEG signals
Nelson et al. Deep-learning-based intelligent neonatal seizure identification using spatial and spectral GNN optimized with the Aquila algorithm
Alsharif et al. Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
Parija et al. Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification
Saminu et al. Hybrid feature extraction technique for multi-classification of ictal and non-ictal EEG epilepsy signals
Bhandari et al. A systematic review of computational intelligence techniques for channel selection in P300-based brain computer interface speller
Chatterjee et al. AI Approaches to Investigate EEG Signal Classification for Cognitive Performance Assessment
Rajendran et al. Neural network based seizure detection system using statistical package analysis
Samarpita et al. Differentiating mental stress levels: Analysing machine learning algorithms comparatively for eeg-based mental stress classification using mne-python
Du et al. EEG-based epileptic seizure detection model using CNN feature optimization
Li Electroencephalography signal analysis and classification based on deep learning
Samiei et al. A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders
Mamtha et al. EEG Signal processing and identification of P300 signals using deep learning
Sukemi et al. Accuracy of neural networks in brain wave diagnosis of schizophrenia
Easttom et al. A Comparitive Study of Machine Learning Algorithms for Identifying Mental States from EEG Recordings
Aquino et al. Stacking ensemble learning approaches applied to emotional state classification
Ye et al. Identification of mental fatigue in language comprehension tasks based on EEG and deep learning
Sadek et al. Multi-classification of High-Frequency Oscillations Using iEEG Signals and Deep Learning Models