Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

AFM Jaramillo, DM Laverty, DJ Morrow… - Renewable Energy, 2021 - Elsevier
In many countries distributed energy resources (DER)(eg photovoltaics, batteries, wind
turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic …

Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies

A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
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 …

Focal and non-focal epilepsy localization: A review

AF Hussein, N Arunkumar, C Gomes… - IEEE …, 2018 - ieeexplore.ieee.org
The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which
has affected million people in the world. Hence, an early detection of the focal epileptic …

EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers

M Omidvar, A Zahedi, H Bakhshi - Journal of ambient intelligence and …, 2021 - Springer
Epilepsy is a neurobiological disease caused by abnormal electrical activity of the human
brain. It is important to detect the epileptic seizures to help the epileptic patients. Using brain …

A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning …

HU Amin, MZ Yusoff, RF Ahmad - Biomedical Signal Processing and …, 2020 - Elsevier
Epilepsy, a common neurological disorder, is generally detected by electroencephalogram
(EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming …

[HTML][HTML] Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features

A Malekzadeh, A Zare, M Yaghoobi, HR Kobravi… - Sensors, 2021 - mdpi.com
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …

A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis

Z Chen, G Lu, Z Xie, W Shang - IEEE Access, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) contains important physiological information that can reflect
the activity of human brain, making it useful for epileptic seizure detection and epilepsy …

A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing

AM Karim, MS Güzel, MR Tolun, H Kaya… - Biocybernetics and …, 2019 - Elsevier
This paper proposes a new framework for medical data processing which is essentially
designed based on deep autoencoder and energy spectral density (ESD) concepts. The …

A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy

KA Khan, PP Shanir, YU Khan, O Farooq - Expert Systems with Applications, 2020 - Elsevier
Epilepsy is one of the grave neurological ailments affecting approximately 70 million people
globally. Detection of epileptic attack is commonly carried out by viewing and analysing long …

Epileptic seizure detection with EEG textural features and imbalanced classification based on EasyEnsemble learning

C Sun, H Cui, W Zhou, W Nie, X Wang… - International journal of …, 2019 - World Scientific
Imbalance data classification is a challenging task in automatic seizure detection from
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …