Raach et al., 2018 - Google Patents
Garma modeling of ecg and classification of arrhythmiaRaach et al., 2018
- Document ID
- 371807488939910988
- Author
- Raach O
- Pillai T
- Abdullah A
- Publication year
- Publication venue
- 2018 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)
External Links
Snippet
Computer-assisted arrhythmia detection is crucial for the treatment of cardiac disorders. Electrocardiograms (ECG) are used to study the electric heart activity and diagnose abnormalities in the heart. It is a non-invasive method where the electric signal of the heart is …
- 206010003119 Arrhythmia 0 title abstract description 25
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
-
- 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
- A61B5/72—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
- A61B5/04—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
- A61B5/04017—Analysis of electro-cardiograms, electro-encephalograms, electro-myograms by using digital filtering
-
- 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
-
- 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
- A61B5/72—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
-
- 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
- G06K9/00496—Recognising patterns in signals and combinations thereof
- G06K9/00503—Preprocessing, e.g. filtering
-
- 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
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/02—Stethoscopes
- A61B7/04—Electric stethoscopes
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gupta et al. | Chaos theory: an emerging tool for arrhythmia detection | |
| Wasimuddin et al. | Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey | |
| Gupta et al. | Chaos theory and ARTFA: emerging tools for interpreting ECG signals to diagnose cardiac arrhythmias | |
| Gupta et al. | A novel FrWT based arrhythmia detection in ECG signal using YWARA and PCA | |
| Venkataramanaiah et al. | ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring | |
| Yang | Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals | |
| Gupta et al. | Efficient R-peak detection in electrocardiogram signal based on features extracted using Hilbert transform and Burg method | |
| Pimentel et al. | Heart beat detection in multimodal physiological data using a hidden semi-Markov model and signal quality indices | |
| Krasteva et al. | QRS template matching for recognition of ventricular ectopic beats | |
| Clifford et al. | False alarm reduction in critical care | |
| Salas-Boni et al. | False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal | |
| Zhao et al. | Deep learning based patient-specific classification of arrhythmia on ECG signal | |
| Mazidi et al. | Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study | |
| Tabassum et al. | An approach of cardiac disease prediction by analyzing ECG signal | |
| Suboh et al. | ECG-based detection and prediction models of sudden cardiac death: Current performances and new perspectives on signal processing techniques | |
| Slama et al. | Application of statistical features and multilayer neural network to automatic diagnosis of arrhythmia by ECG signals | |
| Mayapur | Classification of Arrhythmia from ECG Signals using MATLAB | |
| Vimala | Stress causing Arrhythmia detection from ECG Signal using HMM | |
| Singh et al. | A generic and robust system for automated detection of different classes of arrhythmia | |
| Rao et al. | Performance identification of different heart diseases based on neural network classification | |
| Gupta et al. | Nonlinear technique-based ECG signal analysis for improved healthcare systems | |
| Raach et al. | Garma modeling of ecg and classification of arrhythmia | |
| Pimentel et al. | Hidden semi-Markov model-based heartbeat detection using multimodal data and signal quality indices | |
| Kurniawan et al. | Classification of arrhythmias 12-lead ECG signals based on 1 dimensional convolutional neural networks | |
| Kelwade et al. | Comparative study of neural networks for prediction of cardiac arrhythmias |