Thursday , April 11 2024


Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

The dynamics of 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 developed computer-aided diagnosis (CAD) systems, which radiologists greatly used for identifying abnormalities and applying a few feature-extracting methods. The …

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Arrhythmia Detection using Hybrid Features Extracting Strategy

Cardiac arrhythmias are disturbances in the rhythm of the heart manifested by irregularity or by abnormally fast rates (‘tachycardia’) or abnormally slow rates (‘bradycardias’). In the past researchers extracted different features extracting strategies to detect the arrhythmia. Since, signals acquired from subjects suffered with arrhythmia are multivariate, highly nonlinear, nonstationary, …

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Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Objective Epilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity …

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A multi-modal, multi-atlas based approach for Alzheimer detection via machine learning. International Journal of Imaging Systems and Technology

The use of biomarkers for early detection of Alzheimer’s disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to …

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Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

In this paper, we have employed K-d tree algorithmic based Multiscale entropy analysis (MSE) to distinguish the alcoholic subjects from the non-alcoholic. Traditional MSE technique have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O (N2) i.e. …

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Detecting Epileptic Seizure with Different Feature Extracting Strategies using Robust Machine Learning Classification Techniques by Applying Advance Parameter Optimization Approach

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, …

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