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

Patidar et al., 2018 - Google Patents

Arrhythmia classification based on combination of heart rate, auto regressive coefficient and spectral entropy using probabilistic neural network

Patidar et al., 2018

Document ID
4677635756921863223
Author
Patidar V
Srivastava R
Kumar B
Tewari R
Sahai N
Bhatia D
Publication year
Publication venue
2018 15th IEEE India Council International Conference (INDICON)

External Links

Snippet

This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/0452Detecting specific parameters of the electrocardiograph cycle
    • A61B5/046Detecting fibrillation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/0452Detecting specific parameters of the electrocardiograph cycle
    • A61B5/04525Detecting specific parameters of the electrocardiograph cycle by template matching
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7239Details of waveform analysis using differentiation including higher order derivatives
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61NELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00Electrotherapy; Circuits therefor
    • A61N1/18Applying electric currents by contact electrodes
    • A61N1/32Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36Applying electric currents by contact electrodes alternating or intermittent currents for stimulation, e.g. heart pace-makers
    • A61N1/362Heart stimulators
    • A61N1/37Monitoring; Protecting
    • A61N1/3702Monitoring; Protecting a physiological parameter
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61NELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00Electrotherapy; Circuits therefor
    • A61N1/18Applying electric currents by contact electrodes
    • A61N1/32Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36Applying electric currents by contact electrodes alternating or intermittent currents for stimulation, e.g. heart pace-makers
    • A61N1/362Heart stimulators
    • A61N1/3621Heart stimulators for treating or preventing abnormally high heart rate
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61NELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00Electrotherapy; Circuits therefor
    • A61N1/18Applying electric currents by contact electrodes
    • A61N1/32Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39Heart defibrillators
    • A61N1/3956Implantable devices for applying electric shocks to the heart, e.g. for cardioversion
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61NELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00Electrotherapy; Circuits therefor
    • A61N1/18Applying electric currents by contact electrodes
    • A61N1/32Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39Heart defibrillators
    • A61N1/3925Monitoring; Protecting
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/04012Analysis of electro-cardiograms, electro-encephalograms, electro-myograms
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476Electroencephalography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, 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
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271Specific aspects of physiological measurement analysis
    • A61B5/7285Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications

Similar Documents

Publication Publication Date Title
US11647963B2 (en) System and method for predicting acute cardiopulmonary events and survivability of a patient
Jambukia et al. Classification of ECG signals using machine learning techniques: A survey
Berkaya et al. A survey on ECG analysis
Oweis et al. QRS detection and heart rate variability analysis: A survey
Anuradha et al. ANN for classification of cardiac arrhythmias
Kshirsagar et al. Classification of ECG-signals using artificial neural networks
Dallali et al. Fuzzy c-means clustering, Neural Network, WT, and HRV for classification of cardiac arrhythmia
Bashar et al. Atrial fibrillation detection in icu patients: A pilot study on mimic iii data
Kumari et al. Performance evaluation of neural networks and adaptive neuro fuzzy inference system for classification of cardiac arrhythmia
Gopika et al. Single-layer convolution neural network for cardiac disease classification using electrocardiogram signals
Yousefi et al. Automatic detection of premature ventricular contraction based on photoplethysmography using chaotic features and high order statistics
Mykoliuk et al. Machine learning methods in ECG classification
Alim et al. Application of machine learning on ecg signal classification using morphological features
Riasi et al. Prediction of ventricular tachycardia using morphological features of ECG signal
WO2013186634A2 (en) Predicting acute cardiopulmonary events and survivability of a patient
Mohamad et al. Principal component analysis and arrhythmia recognition using elman neural network
Yathish Early detection of cardiac arrhythmia disease using machine learning and iot technologies
Amiruddin et al. Feature reduction and arrhythmia classification via hybrid multilayered perceptron network
Patidar et al. Arrhythmia classification based on combination of heart rate, auto regressive coefficient and spectral entropy using probabilistic neural network
Rivera et al. Cardiovascular conditions classification using adaptive neuro-fuzzy inference system
Tyagi et al. A review on heartbeat classification for arrhythmia detection using ecg signal processing
Ingole et al. Electrocardiogram (ECG) signals feature extraction and classification using various signal analysis techniques
Ouelli et al. Electrocardiogram features extraction and classification for arrhythmia detection
Sharma et al. A comprehensive review on arrhythmia classification using deep learning methods
Benali et al. Cardiac arrhythmia diagnosis using a neuro-fuzzy approach