Kim et al., 2021 - Google Patents
Fast automatic artifact annotator for EEG signals using deep learningKim et al., 2021
View PDF- Document ID
- 5050973705549137399
- Author
- Kim D
- Keene S
- Publication year
- Publication venue
- Biomedical Signal Processing: Innovation and Applications
External Links
Snippet
Electroencephalogram (EEG) is one of the widely used non-invasive brain signal acquisition techniques that measure voltage fluctuations caused by neuron activities in the brain. EEG is typically used to diagnose and monitor disorders such as epilepsy, sleep disorders, and …
- 210000004556 Brain 0 abstract description 17
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
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- 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
-
- 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
- A61B5/72—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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- 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
- A61B5/7253—Details of waveform analysis characterised by using transforms
-
- 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
- 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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
-
- 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
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- 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
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
-
- 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
- A61B5/48—Other medical applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rasheed et al. | Machine learning for predicting epileptic seizures using EEG signals: A review | |
Ay et al. | Automated depression detection using deep representation and sequence learning with EEG signals | |
Yuan et al. | Wave2vec: Deep representation learning for clinical temporal data | |
Xie et al. | Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis | |
Ullah et al. | Classification of arrhythmia in heartbeat detection using deep learning | |
Kaur et al. | Phase space reconstruction of EEG signals for classification of ADHD and control adults | |
Yuan et al. | Wave2vec: Learning deep representations for biosignals | |
Kaya et al. | A new approach for congestive heart failure and arrhythmia classification using angle transformation with LSTM | |
Kim et al. | Fast automatic artifact annotator for EEG signals using deep learning | |
UR et al. | Automated diagnosis of epileptic electroencephalogram using independent component analysis and discrete wavelet transform for different electroencephalogram durations | |
Soni et al. | Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression | |
Taloba et al. | Machine algorithm for heartbeat monitoring and arrhythmia detection based on ECG systems | |
Prakash et al. | RETRACTED ARTICLE: An efficient deep learning based stress monitoring model through wearable devices for health care applications | |
Liu et al. | A review of arrhythmia detection based on electrocardiogram with artificial intelligence | |
Yogarajan et al. | EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network | |
Nawaz et al. | Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals | |
Karri et al. | A real-time cardiac arrhythmia classification using hybrid combination of delta modulation, 1D-CNN and blended LSTM | |
Tripathi et al. | Automatic seizure detection and classification using super-resolution superlet transform and deep neural network-A preprocessing-less method | |
Chawla et al. | Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features | |
Cimr et al. | Enhancing EEG signal analysis with geometry invariants for multichannel fusion | |
Erkuş et al. | A new collective anomaly detection approach using pitch frequency and dissimilarity: Pitchy anomaly detection (PAD) | |
Shen et al. | GA-SVM modeling of multiclass seizure detector in epilepsy analysis system using cloud computing | |
Lu et al. | Entropy‐Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals | |
Bashir et al. | A machine learning framework for Major depressive disorder (MDD) detection using non-invasive EEG signals | |
da Silva Lourenço et al. | Not one size fits all: influence of EEG type when training a deep neural network for interictal epileptiform discharge detection |