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

Mateo-Sotos et al., 2022 - Google Patents

A machine learning-based method to identify bipolar disorder patients

Mateo-Sotos et al., 2022

Document ID
7542857100169585452
Author
Mateo-Sotos J
Torres A
Santos J
Quevedo O
Basar C
Publication year
Publication venue
Circuits, Systems, and Signal Processing

External Links

Snippet

Bipolar disorder is a serious psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Due to the high percentage of people suffering from severe bipolar and depressive disorders, the modelling, characterisation, classification and …
Continue reading at link.springer.com (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
    • 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
    • G06F19/24Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • 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
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • 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

Similar Documents

Publication Publication Date Title
Raj et al. Sparse representation of ECG signals for automated recognition of cardiac arrhythmias
Chatterjee et al. A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment
Dami et al. Predicting cardiovascular events with deep learning approach in the context of the internet of things
Şen et al. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms
Li et al. Spatial-frequency convolutional self-attention network for EEG emotion recognition
Zhang et al. Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers
Saleem et al. Exploring the applications of machine learning in healthcare
Mateo-Sotos et al. A machine learning-based method to identify bipolar disorder patients
Bashir et al. BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting
Hassan et al. Epileptic seizure detection in EEG using mutual information-based best individual feature selection
Vincent Paul et al. Intelligent framework for prediction of heart disease using deep learning
Gavrishchaka et al. Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data
Garg A signal invariant wavelet function selection algorithm
Pławiak et al. Novel methodology for cardiac arrhythmias classification based on long-duration ECG signal fragments analysis
Fabietti et al. Artefact detection in chronically recorded local field potentials: an explainable machine learning-based approach
Keikhosrokiani et al. Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony
Qin et al. EEG signal classification based on improved variational mode decomposition and deep forest
Kirubakaran et al. Echo state learned compositional pattern neural networks for the early diagnosis of cancer on the internet of medical things platform
Huynh et al. Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset
Bozkurt et al. Development of hybrid artificial intelligence based automatic sleep/awake detection
Kuila et al. ECG signal classification to detect heart arrhythmia using ELM and CNN
Vylala et al. Spectral feature and optimization-based actor-critic neural network for arrhythmia classification using ECG signal
Neifar et al. Deep Generative Models for Physiological Signals: A Systematic Literature Review
Priyanga et al. Web analytics support system for prediction of heart disease using naive bayes weighted approach (nbwa)
Chaitanya et al. Cross Subject Myocardial Infarction Detection From Vectorcardiogram Signals Using Binary Harry Hawks Feature Selection and Ensemble Classifiers