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

Prashar et al., 2020 - Google Patents

Novel cardiac arrhythmia processing using machine learning techniques

Prashar et al., 2020

Document ID
17860023244453854418
Author
Prashar N
Sood M
Jain S
Publication year
Publication venue
International Journal of Image and Graphics

External Links

Snippet

Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient's body. Qualitative …
Continue reading at www.worldscientific.com (other versions)

Classifications

    • 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
    • A61B5/04525—Detecting specific parameters of the electrocardiograph cycle by template matching
    • 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
    • A61B5/0468—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
    • 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
    • A61B5/046—Detecting fibrillation
    • 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
    • A61B5/0408—Electrodes specially adapted therefor
    • 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
    • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
    • 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
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG
    • A61B5/0432—Recording apparatus specially adapted therefor
    • 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
    • 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
    • A61B5/04012—Analysis of electro-cardiograms, electro-encephalograms, electro-myograms
    • 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
    • A61B5/04011—Vector-cardiography
    • 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
    • A61B5/04005—Detecting magnetic fields produced by bio-electric currents
    • A61B5/04007—Detecting magnetic fields produced by bio-electric currents specially adapted for magnetocardiographic signals
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • 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
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02411—Detecting, measuring or recording pulse rate or heart rate of foetuses
    • 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
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • 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
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
    • A61B5/7445—Display arrangements, e.g. multiple display units
    • 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

Similar Documents

Publication Publication Date Title
Hammad et al. Detection of abnormal heart conditions based on characteristics of ECG signals
JP7429371B2 (en) Method and system for quantifying and removing asynchronous noise in biophysical signals
Prashar et al. Novel cardiac arrhythmia processing using machine learning techniques
de Chazal et al. Automatic classification of ECG beats using waveform shape and heart beat interval features
Singhal et al. A systematic review on artificial intelligence-based techniques for diagnosis of cardiovascular arrhythmia diseases: Challenges and opportunities
Wu et al. Personalizing a generic ECG heartbeat classification for arrhythmia detection: a deep learning approach
Dang et al. A novel deep convolutional neural network for arrhythmia classification
Sihem et al. An enhanced random forest for cardiac diseases identification based on ECG signal
Tung et al. Multi-lead ECG classification via an information-based attention convolutional neural network
Rajoub Machine learning in biomedical signal processing with ECG applications
Bortolan et al. Rule-based method and deep learning networks for automatic classification of ECG
Sahoo Analysis of ECG signal for Detection of Cardiac Arrhythmias
Zhang Arrhythmias classification based on CNN and LSTM_ATTENTION hybrid model
Liu et al. Automatic Arrhythmia Detection Based on Convolutional Neural Networks.
Jiang et al. Heartbeat classification system based on modified stacked denoising autoencoders and neural networks
Tribhuvanam et al. Performance Evaluation of Classifiers for ECG Signal Analysis
Sahay et al. Computer‐Aided Interpretation of ECG Signal—A Challenge
Ullah et al. One dimensional convolution neural network model for ECG arrhythmia classification
Van Steenkiste Equine electrocardiography revisited: 12-lead recording, vectorcardiography and the power of machine intelligence
Tan et al. A novel feature representation approach for single-lead heartbeat classification based on adaptive Fourier decomposition
Pander et al. Fuzzy-based algorithm for QRS detection
Paralic Detection of QRS complexes using convolutional neural network
Zhang et al. Classification of Arrhythmia Based on Extreme Learning Machine
Vozda et al. Individualization of a vectorcardiographic model by a particle swarm optimization
Li et al. Detection of Premature Ventricular Contractions using 12-lead Dynamic ECG based on Squeeze-Excitation Residual Network