Nothing Special   »   [go: up one dir, main page]

Zhu et al., 2020 - Google Patents

Classification of cardiac abnormalities from ECG signals using SE-ResNet

Zhu et al., 2020

View PDF
Document ID
13414285914881089456
Author
Zhu Z
Wang H
Zhao T
Guo Y
Xu Z
Liu Z
Liu S
Lan X
Sun X
Feng M
Publication year
Publication venue
2020 Computing in Cardiology

External Links

Snippet

In PhysioNet/Computing in Cardiology Challenge 2020, we developed an ensembled model based on SE-ResNet to classify cardiac abnormalities from 12-lead electrocardiogram (ECG) signals. We employed two residual neural network modules with squeeze-and …
Continue reading at physionet.org (PDF) (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
    • 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
    • A61B5/04012—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
    • A61B5/72—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
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476—Electroencephalography
    • 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

Similar Documents

Publication Publication Date Title
Zhu et al. Classification of cardiac abnormalities from ECG signals using SE-ResNet
Wang et al. Deep multi-scale fusion neural network for multi-class arrhythmia detection
Murugesan et al. Ecgnet: Deep network for arrhythmia classification
Li et al. Inter-patient arrhythmia classification with improved deep residual convolutional neural network
Mousavi et al. ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention
US20230143594A1 (en) Systems and methods for reduced lead electrocardiogram diagnosis using deep neural networks and rule-based systems
Liu et al. Automatic identification of abnormalities in 12-lead ECGs using expert features and convolutional neural networks
Al Rahhal et al. Dense convolutional networks with focal loss and image generation for electrocardiogram classification
CN104840186A (en) Evaluation method of autonomic nervous function of patient suffering from CHF (Congestive Heart-Failure)
CN111202512A (en) Electrocardiogram classification method and device based on wavelet transformation and DCNN
Ullah et al. An End‐to‐End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
EP4041073A1 (en) Systems and methods for electrocardiogram diagnosis using deep neural networks and rule-based systems
CN114052744A (en) Electrocardiosignal classification method based on pulse neural network
Zeng et al. Ensemble learning on heartbeat type classification
CN111557660A (en) Arrhythmia identification method under sub-population deep learning framework
Rahuja et al. A deep neural network approach to automatic multi-class classification of electrocardiogram signals
Berger et al. Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges
Oliveira et al. A novel arrhythmia classification method based on convolutional neural networks interpretation of electrocardiogram images
Hsu et al. Multi-label arrhythmia classification from 12-lead electrocardiograms
Bortolan et al. Rule-based method and deep learning networks for automatic classification of ECG
Cai et al. Classifying different dimensional ECGs using deep residual convolutional neural networks
CN116746939A (en) Multi-lead electrocardiosignal multi-label classification method based on multi-feature fusion
Liu et al. Adaptive atrial fibrillation detection focused on atrial activity analysis
Li et al. Arrhythmia classification method based on knowledge distillation and 2d ECG images
CN111543977B (en) Multi-cascade artificial intelligence vagina discharge method based on 12-lead resting electrocardiogram