Li et al., 2021 - Google Patents
Deep learning of simultaneous intracranial and scalp EEG for prediction, detection, and lateralization of mesial temporal lobe seizuresLi et al., 2021
View HTML- Document ID
- 5399294076111238454
- Author
- Li Z
- Fields M
- Panov F
- Ghatan S
- Yener B
- Marcuse L
- Publication year
- Publication venue
- Frontiers in Neurology
External Links
Snippet
In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed …
- 206010010904 Convulsion 0 title abstract description 271
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
-
- 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
-
- 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
- A61B5/40—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
-
- 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
-
- 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
- A61B5/04—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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Dvey-Aharon et al. | Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach | |
Min et al. | Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system | |
Ahmadlou et al. | Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder | |
Kaur et al. | Phase space reconstruction of EEG signals for classification of ADHD and control adults | |
Cao et al. | Exploring resting-state EEG complexity before migraine attacks | |
Li et al. | Deep learning of simultaneous intracranial and scalp EEG for prediction, detection, and lateralization of mesial temporal lobe seizures | |
Zangeneh Soroush et al. | A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory | |
Stojanović et al. | Predicting epileptic seizures using nonnegative matrix factorization | |
Zhang et al. | Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach | |
Ahmad et al. | A hybrid deep learning approach for epileptic seizure detection in EEG signals | |
Soni et al. | Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression | |
Gagliano et al. | Bispectrum and recurrent neural networks: Improved classification of interictal and preictal states | |
Dvey-Aharon et al. | Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes | |
Jirka et al. | Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification | |
Jose et al. | Adaptive rag-bull rider: A modified self-adaptive optimization algorithm for epileptic seizure detection with deep stacked autoencoder using electroencephalogram | |
Shor et al. | EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia | |
Leoni et al. | Single-trial stimuli classification from detected P300 for augmented Brain–Computer Interface: A deep learning approach | |
Chavan et al. | Effective epileptic seizure detection by classifying focal and non-focal EEG signals using human learning optimization-based hidden Markov model | |
Manoharan et al. | Region-wise brain response classification of ASD children using EEG and BiLSTM RNN | |
Erguzel et al. | Machine learning approaches to predict repetitive transcranial magnetic stimulation treatment response in major depressive disorder | |
Sahu | Artificial intelligence system for verification of schizophrenia via theta-EEG rhythm | |
Alotaiby et al. | Epileptic MEG spike detection using statistical features and genetic programming with KNN | |
Sallam et al. | Epilepsy detection from EEG signals using artificial neural network | |
Chen | Decoding pain from brain activity | |
Abderrahim et al. | Epileptic Seizures Detection Using iEEG Signals and Deep Learning Models |