[go: up one dir, main page]

Li et al., 2010 - Google Patents

A novel abnormal ECG beats detection method

Li et al., 2010

Document ID
16734919672056806122
Author
Li A
Wang S
Zheng H
Ji L
Wu J
Publication year
Publication venue
2010 the 2nd international conference on computer and automation engineering (ICCAE)

External Links

Snippet

Automatic detection of life threatening abnormal beats in electrocardiogram (ECG) signal is of importance in many healthcare applications. The ECG beat signal variations in both shape and time impose great challenges to automatic detection tasks. To address those …
Continue reading at ieeexplore.ieee.org (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
    • A61B5/046—Detecting fibrillation
    • 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
    • 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
    • G06K9/00496—Recognising patterns in signals and combinations thereof
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Yeh et al. Analyzing ECG for cardiac arrhythmia using cluster analysis
Jekova et al. Assessment and comparison of different methods for heartbeat classification
CN107122788B (en) A kind of identification method and device based on ECG signal
Faziludeen et al. ECG beat classification using wavelets and SVM
CN109522916A (en) The cascade binary classifier of the rhythm and pace of moving things in electrocardiogram (ECG) signal is singly led in identification
US8137283B2 (en) Method and apparatus for retrieval of similar heart sounds from a database
De Lannoy et al. Weighted SVMs and feature relevance assessment in supervised heart beat classification
CN1951320A (en) Abnormal electrocardiogram recognition method based on ultra-complete characteristics
US8750973B2 (en) Method and system for detecting P-waves in the surface ECG signal
Sedighian et al. Pediatric heart sound segmentation using Hidden Markov Model
CN112932498A (en) T wave morphology classification system with strong generalization capability based on deep learning
Yang et al. Heartbeat classification using discrete wavelet transform and kernel principal component analysis
Wu et al. Personalizing a generic ECG heartbeat classification for arrhythmia detection: a deep learning approach
Yu et al. A switchable scheme for ECG beat classification based on independent component analysis
CN115486849B (en) Electrocardiosignal quality assessment method, device and equipment
Zhu et al. Feature extraction from a novel ECG model for arrhythmia diagnosis
Kundu et al. Electrocardiogram synthesis using Gaussian and fourier models
Fotoohinasab et al. A graph-constrained changepoint detection approach for ECG segmentation
CN112370065A (en) Identity recognition method, system and medium based on electrocardiosignal dynamics characteristics
Li et al. A novel abnormal ECG beats detection method
Wang et al. Finding disease similarity by combining ECG with heart auscultation sound
Mayapur Classification of Arrhythmia from ECG Signals using MATLAB
Al-Masri Detecting ECG heartbeat abnormalities using artificial neural networks
Shantha Selva Kumari et al. Classification of cardiac arrhythmias based on morphological and rhythmic features
Don et al. Analysis of electrocardiogram signals of arrhythmia and ischemia using fractal and statistical features