A review on machine learning for EEG signal processing in bioengineering
Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …
conditions in patients since its discovery. Due to the many different types of classifiers …
[HTML][HTML] Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Electroencephalography (EEG) is an important tool for studying the human brain activity and
epileptic processes in particular. EEG signals provide important information about …
epileptic processes in particular. EEG signals provide important information about …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
EMD-based temporal and spectral features for the classification of EEG signals using supervised learning
This paper presents a novel method for feature extraction from electroencephalogram (EEG)
signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the …
signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the …
Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy
H Ocak - Expert Systems with Applications, 2009 - Elsevier
In this study, a new scheme was presented for detecting epileptic seizures from electro-
encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new …
encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new …
A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms
Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology.
Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study …
Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study …
Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network
There are numerous neurological disorders such as dementia, headache, traumatic brain
injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological …
injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological …
Automated epileptic seizure detection methods: a review study
Epilepsy is a neurological disorder with prevalence of about 1-2% of the world's population
(Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and …
(Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and …
Classification of EMG signals using combined features and soft computing techniques
A Subasi - Applied soft computing, 2012 - Elsevier
The motor unit action potentials (MUPs) in an electromyographic (EMG) signal provide a
significant source of information for the assessment of neuromuscular disorders. Since …
significant source of information for the assessment of neuromuscular disorders. Since …
Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis
We present a new technique for detection of epileptiform activity in EEG signals. After
preprocessing of EEG signals we extract representative features in time, frequency and time …
preprocessing of EEG signals we extract representative features in time, frequency and time …