Lofhede et al., 2007 - Google Patents

Comparison of three methods for classifying burst and suppression in the EEG of post asphyctic newborns

Lofhede et al., 2007

View PDF
Document ID
12097134580227531427
Author
Lofhede J
Lofgren N
Thordstein M
Flisberg A
Kjellmer I
Lindecrantz K
Publication year
Publication venue
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

External Links

Snippet

Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features …
Continue reading at 139.91.210.27 (PDF) (other versions)

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal 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
    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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
    • 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
    • A61B5/72Signal 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
    • 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
    • A61B5/7253Details of waveform analysis characterised by using transforms
    • A61B5/726Details of waveform analysis characterised by using transforms using Wavelet transforms
    • 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
    • A61B5/7225Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • 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
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4818Sleep apnoea

Similar Documents

Publication Publication Date Title
US8298140B2 (en) Analysis of EEG signals to detect hypoglycaemia
Ghosh et al. Automatic eyeblink and muscular artifact detection and removal from EEG signals using k-nearest neighbor classifier and long short-term memory networks
EP1143855B1 (en) Method for the quantification of human alertness
Aarabi et al. A fuzzy rule-based system for epileptic seizure detection in intracranial EEG
Aarabi et al. A multistage knowledge-based system for EEG seizure detection in newborn infants
Dobrowolski et al. Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders
Löfhede et al. Automatic classification of background EEG activity in healthy and sick neonates
Bono et al. Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG
Sivasankari et al. An improved EEG signal classification using neural network with the consequence of ICA and STFT
Dornhege et al. Optimizing spatio-temporal filters for improving brain-computer interfacing
Löfhede et al. Classification of burst and suppression in the neonatal electroencephalogram
Furui et al. Non-Gaussianity detection of EEG signals based on a multivariate scale mixture model for diagnosis of epileptic seizures
Argoud et al. SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information
Keshavamurthy et al. Review paper on denoising of ECG signal
Greene et al. Classifier models and architectures for EEG-based neonatal seizure detection
Velvizhy et al. Detection of epileptic seizure using hybrid machine learning algorithms
Temko et al. Instantaneous measure of EEG channel importance for improved patient-adaptive neonatal seizure detection
Goshvarpour et al. Classification of heart rate signals during meditation using Lyapunov exponents and entropy
Lofhede et al. Comparison of three methods for classifying burst and suppression in the EEG of post asphyctic newborns
Khoshnoud et al. Non-linear EEG analysis in children with attention-deficit/hyperactivity disorder during the rest condition
Lofhede et al. Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns using a Support Vector Machine
Jayagopi et al. On the classification of arrhythmia using supplementary features from tetrolet transforms
Dora et al. Engineering approaches for ECG artefact removal from EEG: a review
Chendeb et al. Classification of non stationary signals using multiscale decomposition
Awang et al. Implementing eigen features methods/neural network for EEG signal analysis