A comprehensive review of machine learning approaches for dyslexia diagnosis

N Ahire, RN Awale, S Patnaik, A Wagh - Multimedia Tools and …, 2023 - Springer
Electroencephalography (EEG) is the commonly employed electro-biological imaging
technique for diagnosing brain functioning. The EEG signals are used to determine head …

Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review

A Miltiadous, KD Tzimourta, N Giannakeas… - IEEE …, 2022 - ieeexplore.ieee.org
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …

[HTML][HTML] Classification of EEG signals for prediction of epileptic seizures

MH Aslam, SM Usman, S Khalid, A Anwar… - Applied Sciences, 2022 - mdpi.com
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a
single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal …

Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features

S Pattnaik, N Rout, S Sabut - International Journal of Information …, 2022 - Springer
Epilepsy is a prevalent neurological disorder among numerous neurons degenerative
diseases after brain stroke. During a seizure event, there are bursts of electrical activity in …

[HTML][HTML] Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method

A Malekzadeh, A Zare, M Yaghoobi… - Big Data and Cognitive …, 2021 - mdpi.com
This paper proposes a new method for epileptic seizure detection in
electroencephalography (EEG) signals using nonlinear features based on fractal dimension …

A dynamic filtering DF-RNN deep-learning-based approach for EEG-based neurological disorders diagnosis

G Bouallegue, R Djemal, SA Alshebeili… - IEEE …, 2020 - ieeexplore.ieee.org
Filtering of unwanted signals has a great impact on the performance of EEG signal
processing applied to neurological disorders diagnosis. It is so difficult to remove …

[HTML][HTML] Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

S Abdullah, SS Abosuliman - Scientific Reports, 2023 - nature.com
Electroencephalograms (EEG) is used to assess patients' clinical records of depression
(EEG). The disorder of human thinking is a very complex problem caused by heavy-duty in …

[HTML][HTML] Automatic seizure classification based on domain-invariant deep representation of EEG

X Cao, B Yao, B Chen, W Sun, G Tan - Frontiers in Neuroscience, 2021 - frontiersin.org
Accurate identification of the type of seizure is very important for the treatment plan and drug
prescription of epileptic patients. Artificial intelligence has shown considerable potential in …

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

BK Pradhan, BC Neelappu… - Journal of …, 2023 - Wiley Online Library
The joint time‐frequency analysis method represents a signal in both time and frequency.
Thus, it provides more information compared to other one‐dimensional methods. Several …

GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization

W Li, H Wang, L Zhuang - Expert Systems With Applications, 2023 - Elsevier
There is growing evidence that an increasing number of people suffer from mental illness,
which seriously affects their quality of life. The study of electroencephalography (EEG) is …