Moro et al., 2023 - Google Patents
Automatic video analysis and classification of sleep‐related hypermotor seizures and disorders of arousalMoro et al., 2023
View PDF- Document ID
- 13952634569661730065
- Author
- Moro M
- Pastore V
- Marchesi G
- Proserpio P
- Tassi L
- Castelnovo A
- Manconi M
- Nobile G
- Cordani R
- Gibbs S
- Odone F
- Casadio M
- Nobili L
- Publication year
- Publication venue
- Epilepsia
External Links
Snippet
Objective Sleep‐related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective …
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
- 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
-
- 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
-
- 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
- G06K9/6267—Classification techniques
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
- G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
-
- 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
- G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
-
- 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
- A61B5/16—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 |
|---|---|---|
| Moro et al. | Automatic video analysis and classification of sleep‐related hypermotor seizures and disorders of arousal | |
| Zunino et al. | Video gesture analysis for autism spectrum disorder detection | |
| Dvey-Aharon et al. | Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach | |
| Dima et al. | Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces | |
| Mengi et al. | Artificial intelligence based techniques for the detection of socio-behavioral disorders: a systematic review | |
| Karácsony et al. | Novel 3D video action recognition deep learning approach for near real time epileptic seizure classification | |
| Fan et al. | DC-tCNN: A deep model for EEG-based detection of dim targets | |
| Raizada et al. | Pattern‐information fMRI: New questions which it opens up and challenges which face it | |
| Wu et al. | Tic detection in tourette syndrome patients based on unsupervised visual feature learning | |
| Shi et al. | Categorizing objects from MEG signals using EEGNet | |
| Papadimitriou et al. | Visual representation decoding from human brain activity using machine learning: A baseline study | |
| Fernández et al. | A convolutional neural network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children | |
| Brown et al. | Computer vision for automated seizure detection and classification: A systematic review | |
| Rodrigues et al. | Identification of Parkinson’s disease through facial image classification: a systematic review | |
| Yargholi et al. | Two distinct networks containing position-tolerant representations of actions in the human brain | |
| ArulDass et al. | Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace prior and graph neural networks | |
| Sherbakov | Computational principles for an autonomous active vision system | |
| Ma et al. | Cognition-Supervised Saliency Detection: Contrasting EEG Signals and Visual Stimuli | |
| Mahmoud et al. | Classifying a type of brain disorder in children: an effective fMRI based deep attempt | |
| Sahin et al. | A Multimodal Convolutional Neural Network Model for Parkinson’s Disease Diagnosis Based on Fused Handwriting Dynamics Signals | |
| Zhang et al. | A Generative Adversarial Network-Based Method for Synthesizing Tremor Data in Parkinson’s Disease | |
| Ma et al. | Cognition-supervised learning: Contrasting eeg signals and visual stimuli for saliency detection | |
| Embleton et al. | Northumbria research link | |
| Yao et al. | Eye movement and visual target synchronization level detection using deep learning | |
| Valarmathi et al. | A Multi‐Model Approach for Attention Prediction in Gaming Environments for Autistic Children |