Stress detection using ECG and EMG signals: A comprehensive study
Abstract Background and Objective In recent years, stress and mental health have been
considered as important worldwide concerns. Stress detection using physiological signals …
considered as important worldwide concerns. Stress detection using physiological signals …
Medical health big data classification based on KNN classification algorithm
W **ng, Y Bei - Ieee Access, 2019 - ieeexplore.ieee.org
The rapid development of information technology has led to the development of medical
informatization in the direction of intelligence. Medical health big data provides a basic data …
informatization in the direction of intelligence. Medical health big data provides a basic data …
A survey on semi-supervised feature selection methods
Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …
which eliminates irrelevant and redundant features and improves learning performance. In …
[HTML][HTML] Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a
low-altitude airborne platform is investigated for the detection of plant stress caused by …
low-altitude airborne platform is investigated for the detection of plant stress caused by …
Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature …
We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking
and a genetic algorithm to analyze structural magnetic resonance imaging data; using this …
and a genetic algorithm to analyze structural magnetic resonance imaging data; using this …
Epilepsy detection from EEG signals: a review
A Sharmila - Journal of medical engineering & technology, 2018 - Taylor & Francis
Over many decades, research is being attempted for the detection of epileptic seizure to
support for automatic diagnosis system to help clinicians from burdensome work. In this …
support for automatic diagnosis system to help clinicians from burdensome work. In this …
Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier
Computer-aided heartbeat classification has a significant role in the diagnosis of cardiac
dysfunction. Electrocardiogram (ECG) provides vital information about the heartbeats. In this …
dysfunction. Electrocardiogram (ECG) provides vital information about the heartbeats. In this …
Emotion classification from speech signal based on empirical mode decomposition and non-linear features: Speech emotion recognition
Emotion recognition system from speech signal is a widely researched topic in the design of
the Human–Computer Interface (HCI) models, since it provides insights into the mental …
the Human–Computer Interface (HCI) models, since it provides insights into the mental …
Predicting MCI to AD conversation using integrated sMRI and rs-fMRI: machine learning and graph theory approach
T Zhang, Q Liao, D Zhang, C Zhang, J Yan… - Frontiers in Aging …, 2021 - frontiersin.org
Background Graph theory and machine learning have been shown to be effective ways of
classifying different stages of Alzheimer's disease (AD). Most previous studies have only …
classifying different stages of Alzheimer's disease (AD). Most previous studies have only …
NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms
Nowadays, humanity is facing one of the most dangerous pandemics known as COVID-19.
Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the …
Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the …