A comparative analysis of signal processing and classification methods for different applications based on EEG signals
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020 - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …
currents that are generated due to the synchronized activity by a group of specialized …
Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies
Prostate is a second leading causes of cancer deaths among men. Early detection of cancer
can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and …
can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and …
Detecting brain tumor using machines learning techniques based on different features extracting strategies
Background: Brain tumor is the leading cause of death worldwide. It is obvious that the
chances of survival can be increased if the tumor is identified and properly classified at an …
chances of survival can be increased if the tumor is identified and properly classified at an …
Lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features
In the present era, cancer is the leading cause of demise in both men and women
worldwide, with low survival rates due to inefficient diagnostic techniques. Recently …
worldwide, with low survival rates due to inefficient diagnostic techniques. Recently …
Regression analysis for detecting epileptic seizure with different feature extracting strategies
Due to the excitability of neurons in the brain, a neurological disorder is produced known as
epilepsy. The brain activity of patients suffering from epilepsy is monitored through …
epilepsy. The brain activity of patients suffering from epilepsy is monitored through …
[PDF][PDF] Machine learning based congestive heart failure detection using feature importance ranking of multimodal features
In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure
(CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 …
(CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 …
Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust …
The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans
over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal …
over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal …
Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features
Lung cancer is the major cause of cancer-related deaths worldwide with poor survival due to
the poor diagnostic system at the advanced cancer stage. In the past, researchers …
the poor diagnostic system at the advanced cancer stage. In the past, researchers …
Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches
J Gancio, C Masoller, G Tirabassi - Chaos: An Interdisciplinary Journal …, 2024 - pubs.aip.org
Developing reliable methodologies to decode brain state information from
electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG …
electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG …
The reliability and psychometric structure of Multi-Scale Entropy measured from EEG signals at rest and during face and object recognition tasks
Abstract Background Multi-Scale Entropy (MSE) is a widely used marker of Brain Signal
Complexity (BSC) at multiple temporal scales. Methodological improvement There is no …
Complexity (BSC) at multiple temporal scales. Methodological improvement There is no …