Automated seizure prediction

UR Acharya, Y Hagiwara, H Adeli - Epilepsy & Behavior, 2018 - Elsevier
In the past two decades, significant advances have been made on automated
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …

Machine learning and multiresolution decomposition for embedded applications to detect short-circuit in induction motors

RGC Cunha, ET da Silva Jr, CM de Sá Medeiros - Computers in Industry, 2021 - Elsevier
Due to the relevance of induction machines (IM) in industrial applications, the development
of solutions to predict and detect incipient faults in such equipment is an important field of …

An overview of machine learning and deep learning techniques for predicting epileptic seizures

M Zurdo-Tabernero, Á Canal-Alonso… - Journal of Integrative …, 2024 - degruyter.com
Epilepsy is a neurological disorder (the third most common, following stroke and migraines).
A key aspect of its diagnosis is the presence of seizures that occur without a known cause …

A signal invariant wavelet function selection algorithm

G Garg - Medical & biological engineering & computing, 2016 - Springer
This paper addresses the problem of mother wavelet selection for wavelet signal processing
in feature extraction and pattern recognition. The problem is formulated as an optimization …

Using new neighborhood-based intensity-scale verification metrics to evaluate WRF precipitation forecasts at 4 and 12 km grid spacings

B Yu, K Zhu, M Xue, B Zhou - Atmospheric Research, 2020 - Elsevier
Wavelet-decomposition-based intensity-scale skill (ISS) score is a verification metric which
decomposes the forecast fields into different scales and then calculates verification scores …

Machine Learning and Deep Learning Techniques for Epileptic Seizures Prediction: A Brief Review

M Hernández, Á Canal-Alonso, F de la Prieta… - … Conference on Practical …, 2022 - Springer
The third most common neurological disorder, only behind stroke and migraines, is
Epilepsy. The main criteria for its diagnosis are the occurrence of unprovoked seizures and …

[PDF][PDF] Wavelet energy based neural fuzzy model for automatic motor imagery classification

G Garg, S Suri, R Garg, V Singh - International Journal of Computer …, 2011 - Citeseer
Brain-computer interface (BCI) is a communication system by which a person can send
messages without any use of peripheral nerves and muscles. BCI systems might help to …

[PDF][PDF] Classification of multi heart diseases with android based monitoring system

A Al-Obaidi, SH Alnajjar, M Nsai, H Sharabaty - Iraqi Journal of Computers …, 2020 - iasj.net
Electrocardiogram (ECG) examination via computer techniques that involve feature
extraction, pre-processing and post-processing was implemented due to its significant …

[PDF][PDF] PERFORMANCE IMPROVEMENT WITH OPTIMIZATION OF MACHINE LEARNING METHODS FOR SLEEP STAGE CLASSIFICATION FROM EEG SIGNALS

E TUNCER, ED BOLAT - INTERNATIONAL RESEARCH IN … - researchgate.net
Every human spends one third of his life sleeping. This rate increases in infancy, childhood,
and youth, and decreases in adulthood and advanced ages. Sleep is the period when the …

[PDF][PDF] Meta Heuristics based Machine Learning and Neural Mass Modelling Allied to Brain Machine Interface

E Zareian - 2021 - repository.lincoln.ac.uk
New understanding of the brain function and increasing availability of low-cost-non-invasive
electroencephalograms (EEGs) recording devices have made brain-computer-interface …