Lin et al., 2020 - Google Patents
Explainable deep neural network for identifying cardiac abnormalities using class activation mapLin et al., 2020
View PDF- Document ID
- 8060886596082604877
- Author
- Lin Y
- Lee Y
- Tsai W
- Beh W
- Wu A
- Publication year
- Publication venue
- 2020 Computing in Cardiology
External Links
Snippet
In this study, our team “NTU-Accesslab” present a deep convolutional neural network (CNN) approach, called CNN-GAP, for classifying 12-lead ECGs with multilabel cardiac abnormalities. Additionally, Class Activation Mapping (CAM) is employed for further …
- 230000004913 activation 0 title abstract description 12
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
- A61B5/04—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
- A61B5/72—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
-
- 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
- G06K9/00496—Recognising patterns in signals and combinations thereof
- G06K9/00536—Classification; Matching
- G06K9/00543—Classification; Matching by matching peak patterns
-
- 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
-
- 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
- G06K9/6267—Classification techniques
-
- 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
- A61B5/0531—Measuring skin impedance
-
- 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
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cai et al. | Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network | |
Jung et al. | Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing | |
Zhai et al. | Automated ECG classification using dual heartbeat coupling based on convolutional neural network | |
Padhy et al. | Third-order tensor based analysis of multilead ECG for classification of myocardial infarction | |
US10602942B2 (en) | Method of detecting abnormalities in ECG signals | |
Belgacem et al. | A novel biometric authentication approach using ECG and EMG signals | |
US20230143594A1 (en) | Systems and methods for reduced lead electrocardiogram diagnosis using deep neural networks and rule-based systems | |
CN109171712A (en) | Auricular fibrillation recognition methods, device, equipment and computer readable storage medium | |
Tadesse et al. | Cardiovascular disease diagnosis using cross-domain transfer learning | |
Li et al. | Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network | |
Le et al. | Multi-module recurrent convolutional neural network with transformer encoder for ECG arrhythmia classification | |
US11571161B2 (en) | Systems and methods for electrocardiogram diagnosis using deep neural networks and rule-based systems | |
Khan et al. | Electrocardiogram heartbeat classification using convolutional neural networks for the detection of cardiac Arrhythmia | |
Lin et al. | Explainable deep neural network for identifying cardiac abnormalities using class activation map | |
Pinto et al. | Explaining ECG biometrics: Is it all in the QRS? | |
Wang et al. | Multiscale residual network based on channel spatial attention mechanism for multilabel ECG classification | |
Berger et al. | Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges | |
De Marco et al. | Classification of premature ventricular contraction using deep learning | |
Li et al. | Diagnosis of atrial fibrillation based on lightweight detail-semantic network | |
Lee et al. | Using beat score maps with successive segmentation for ECG classification without R-peak detection | |
Ostertag et al. | Reconstructing ECG precordial leads from a reduced lead set using independent component analysis | |
Yao et al. | Arrhythmia classification from single lead ecg by multi-scale convolutional neural networks | |
Caldas et al. | A new methodology for classifying qrs morphology in ecg signals | |
Huerta et al. | Comparison of pre-trained deep learning algorithms for quality assessment of electrocardiographic recordings | |
Ghorai et al. | Arrhythmia classification by nonlinear kernel-based ECG signal modeling |