Sabrine et al., 2022 - Google Patents
Arrhythmia Classification Using Fractal Dimensions and Neural NetworksSabrine et al., 2022
View PDF- Document ID
- 3115488843361257418
- Author
- Sabrine B
- Taoufik A
- Publication year
- Publication venue
- 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
External Links
Snippet
According to statistics, there has been a big increment in death in consequence of failures worldwide. Electrocardiogram was chosen as a possible implement for diagnosing cardiovascular diseases, it is a test that records the electrical activity given by the heart …
- 230000001537 neural 0 title abstract description 19
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
- A61B5/72—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
- A61B5/0452—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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
-
- 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
- A61B5/02—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
- A61B5/72—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
-
- 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
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- 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
-
- 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
- A61B5/48—Other medical applications
-
- 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
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/164—Lie detection
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Oh et al. | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats | |
Hagiwara et al. | Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review | |
Acharya et al. | Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network | |
Murugesan et al. | Ecgnet: Deep network for arrhythmia classification | |
Ge et al. | Cardiac arrhythmia classification using autoregressive modeling | |
Martis et al. | Application of higher order statistics for atrial arrhythmia classification | |
Sansone et al. | Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review | |
Abibullaev et al. | A new QRS detection method using wavelets and artificial neural networks | |
Yang | Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals | |
Mousavi et al. | ECG Language processing (ELP): A new technique to analyze ECG signals | |
Gupta et al. | PCA as an effective tool for the detection of R-peaks in an ECG signal processing | |
Karri et al. | A real-time embedded system to detect QRS-complex and arrhythmia classification using LSTM through hybridized features | |
Mahesh et al. | ECG arrhythmia classification based on logistic model tree | |
Kumari et al. | Performance evaluation of neural networks and adaptive neuro fuzzy inference system for classification of cardiac arrhythmia | |
Reddy et al. | Classification of arrhythmia disease through electrocardiogram signals using sampling vector random forest classifier | |
Begum et al. | Automated detection of abnormalities in ECG signals using deep neural network | |
Alim et al. | Application of machine learning on ecg signal classification using morphological features | |
Krak et al. | Electrocardiogram classification using wavelet transformations | |
Sharma et al. | An intelligent deep neural network with Opposition based Laplacian Equilibrium Optimizer to improve feature extraction using ECG signals | |
Sabrine et al. | Arrhythmia Classification Using Fractal Dimensions and Neural Networks | |
Oliveira et al. | A novel arrhythmia classification method based on convolutional neural networks interpretation of electrocardiogram images | |
Huerta et al. | Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots | |
Ekhlasi et al. | Analysis of ECG signals to classify abnormal patterns by employing Artificial Neural Network and Discrete Wavelet Coefficients | |
Rodrigues et al. | The issue of automatic classification of heartbeats | |
Jangra et al. | Impact of feature extraction techniques on cardiac arrhythmia classification: experimental approach |