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

Wang et al., 2017 - Google Patents

Clustering ECG heartbeat using improved semi‐supervised affinity propagation

Wang et al., 2017

View PDF @Full View
Document ID
54930343414512613
Author
Wang L
Zhou X
Xing Y
Yang M
Zhang C
Publication year
Publication venue
Iet Software

External Links

Snippet

The electrocardiogram (ECG) has become an important tool for the diagnosis of cardiovascular diseases. As long‐term ECG recordings become more common, driven partly by the development of intelligent hardware, the requirement for automatic ECG analysis …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • 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
    • 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
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management

Similar Documents

Publication Publication Date Title
Wang et al. Arrhythmia classification algorithm based on multi-head self-attention mechanism
Strodthoff et al. Detecting and interpreting myocardial infarction using fully convolutional neural networks
Jing et al. ECG heartbeat classification based on an improved ResNet‐18 model
Al Rahhal et al. Deep learning approach for active classification of electrocardiogram signals
Hanbay Deep neural network based approach for ECG classification using hybrid differential features and active learning
Ullah et al. Classification of arrhythmia in heartbeat detection using deep learning
Yao et al. Interpretation of electrocardiogram heartbeat by CNN and GRU
Cai et al. Real‐Time Arrhythmia Classification Algorithm Using Time‐Domain ECG Feature Based on FFNN and CNN
Sharma et al. Efficient methodology for electrocardiogram beat classification
Leite et al. Heartbeat classification with low computational cost using Hjorth parameters
Balouchestani et al. Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach
Wang et al. Clustering ECG heartbeat using improved semi‐supervised affinity propagation
Moses et al. A survey of data mining algorithms used in cardiovascular disease diagnosis from multi-lead ECG data
Fang et al. Electrocardiogram signal classification in the diagnosis of heart disease based on RBF neural network
Singh et al. Block sparsity‐based joint compressed sensing recovery of multi‐channel ECG signals
Liu et al. A review of arrhythmia detection based on electrocardiogram with artificial intelligence
Lyakhov et al. System for neural network determination of atrial fibrillation on ECG signals with wavelet-based preprocessing
Huang et al. A multiview feature fusion model for heartbeat classification
Andrysiak Machine learning techniques applied to data analysis and anomaly detection in ECG signals
Übeyli Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats
Yu et al. LPClass: lightweight personalized sensor data classification in computational social systems
Urteaga et al. A machine learning model for the prognosis of pulseless electrical activity during out-of-hospital cardiac arrest
Liao et al. Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network
Wang et al. Multiscale residual network based on channel spatial attention mechanism for multilabel ECG classification
Lu et al. Identification of arrhythmia by using a decision tree and gated network fusion model