Parsi et al., 2019 - Google Patents
Prediction of sudden cardiac death in implantable cardioverter defibrillators: a review and comparative study of heart rate variability featuresParsi et al., 2019
View PDF- Document ID
- 3161003843935595918
- Author
- Parsi A
- O’Loughlin D
- Glavin M
- Jones E
- Publication year
- Publication venue
- IEEE Reviews in Biomedical Engineering
External Links
Snippet
Over the last four decades, implantable cardioverter defibrillators (ICDs) have been widely deployed to reduce sudden cardiac death (SCD) risk in patients with a history of life- threatening arrhythmia. By continuous monitoring of the heart rate, ICDs can use decision …
- 208000007322 Death, Sudden, Cardiac 0 title abstract description 37
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
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation, e.g. heart pace-makers
- A61N1/362—Heart stimulators
- A61N1/37—Monitoring; Protecting
- A61N1/3702—Monitoring; Protecting a physiological parameter
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
- A61N1/39—Heart defibrillators
- A61N1/3925—Monitoring; Protecting
-
- 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
- A61B5/0476—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
-
- 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
- A61B5/02—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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ebrahimzadeh et al. | A time local subset feature selection for prediction of sudden cardiac death from ECG signal | |
Lee et al. | Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks | |
Tripathy et al. | Detection of shockable ventricular arrhythmia using variational mode decomposition | |
Parsi et al. | Prediction of sudden cardiac death in implantable cardioverter defibrillators: a review and comparative study of heart rate variability features | |
Lin | Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier | |
Sansone et al. | Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review | |
Martis et al. | Application of higher order statistics for atrial arrhythmia classification | |
Oweis et al. | QRS detection and heart rate variability analysis: A survey | |
Murukesan et al. | Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features | |
Amann et al. | Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators | |
Anuradha et al. | CARDIAC ARRHYTHMIA CLASSIFICATION USING FUZZY CLASSIFIERS. | |
US20120232417A1 (en) | Signal Analysis System for Heart Condition Determination | |
Anas et al. | Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions | |
Al-Fahoum et al. | A practical reconstructed phase space approach for ECG arrhythmias classification | |
Tseng et al. | Predicting ventricular fibrillation through deep learning | |
Mohanty et al. | Machine learning approach to recognize ventricular arrhythmias using VMD based features | |
Mazidi et al. | Premature ventricular contraction (PVC) detection system based on tunable Q-factor wavelet transform | |
Shi et al. | Automated atrial fibrillation detection based on feature fusion using discriminant canonical correlation analysis | |
Chen et al. | The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals | |
Kumari et al. | Performance evaluation of neural networks and adaptive neuro fuzzy inference system for classification of cardiac arrhythmia | |
Mahesh et al. | ECG arrhythmia classification based on logistic model tree | |
Alcaraz et al. | Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings | |
US8849386B2 (en) | Analyzing electrocardiograms | |
Parsi et al. | Heart rate variability analysis to predict onset of ventricular tachyarrhythmias in implantable cardioverter defibrillators | |
Lee | Development of ventricular fibrillation diagnosis method based on neuro-fuzzy systems for automated external defibrillators |