[go: up one dir, main page]

Wijayanto et al., 2019 - Google Patents

A study on signal complexity measurement for epileptic seizure detection

Wijayanto et al., 2019

Document ID
14738198874994079498
Author
Wijayanto I
Hartanto R
Nugroho H
Setiawan N
Publication year
Publication venue
2019 IEEE 9th International Conference on System Engineering and Technology (ICSET)

External Links

Snippet

Epilepsy is a sudden brain malfunction that represented by an exaggerated, high, and hypersynchronous neuron activity inside the brain. Electroencephalogram (EEG) is a tool that can be used to measure the electrical changes of the brain by using electrodes placed …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • A61B5/7235—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
    • 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
    • G06K9/00536—Classification; Matching
    • G06K9/0055—Classification; Matching by matching signal segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier 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
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms

Similar Documents

Publication Publication Date Title
Savadkoohi et al. A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal
Borowska Entropy-based algorithms in the analysis of biomedical signals
Slimen et al. EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms
Song et al. Automatic recognition of epileptic EEG patterns via extreme learning machine and multiresolution feature extraction
Kumar et al. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine
Song et al. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine
Ocak Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy
Indiradevi et al. A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram
Sharanreddy et al. EEG signal classification for epilepsy seizure detection using improved approximate entropy
Wijayanto et al. Multilevel wavelet packet entropy and support vector machine for epileptic EEG classification
Nagaraj et al. Neonatal seizure detection using atomic decomposition with a novel dictionary
Ahmad et al. A secure and interpretable AI for smart healthcare system: A case study on epilepsy diagnosis using EEG signals
Gupta et al. Detection of epileptic seizures using eeg signals
Kumar et al. Complexity measures for normal and epileptic EEG signals using ApEn, SampEn and SEN
Fasil et al. Epileptic seizure classification using shifting sample difference of EEG signals
Lee et al. Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals
Jacob et al. Diagnosis of epilepsy from interictal EEGs based on chaotic and wavelet transformation
Lian et al. Spatial enhanced pattern through graph convolutional neural network for epileptic EEG identification
Rizal et al. Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine.
Wijayanto et al. A study on signal complexity measurement for epileptic seizure detection
Alahmadi et al. Prediction of mild cognitive impairment using EEG signal and BiLSTM network
Benzaid et al. Seizure detection using nonlinear measures over EEG frequency bands and deep learning classifiers
Hasnaoui et al. Discrete wavelet transform and sample entropy-based EEG dimensionality reduction for electroencephalogram classification
Lee et al. Classification of both seizure and non-seizure based on EEG signals using hidden Markov model
Salankar et al. Classification of seizure and seizure free EEG signals using optimal mother wavelet and relative power