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Heart rate variability: a review
TLDR
The various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV are discussed.
Automated detection of COVID-19 cases using deep neural networks with X-ray images
TLDR
A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
Automated diagnosis of epileptic EEG using entropies
TLDR
This work proposes a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals and shows that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%.
ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform
TLDR
Five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed and dimensionality reduced features were fed to the Support Vector Machine, neural network and probabilistic neural network (PNN) classifiers for automated diagnosis.
A deep convolutional neural network model to classify heartbeats
TLDR
A 9-layer deep convolutional neural network (CNN) is developed to automatically identify 5 different categories of heartbeats in ECG signals to serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmicheartbeats.
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
TLDR
In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
Entropies for detection of epilepsy in EEG
TLDR
The results obtained indicate that entropy estimators can distinguish normal and epileptic EEG data with more than 95% confidence (using t-test), and the classification ability of the entropy measures is tested using ANFIS classifier.
EEG Signal Analysis: A Survey
TLDR
The effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail.
Wavelet-Based Energy Features for Glaucomatous Image Classification
TLDR
This paper proposes a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies, achieving an accuracy of around 93% using tenfold cross validations.
Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features
TLDR
A novel method for glaucoma detection using a combination of texture and higher order spectra (HOS) features from digital fundus images is presented and it is demonstrated that the texture and HOS features after z-score normalization and feature selection, and when combined with a random-forest classifier, performs better than the other classifiers.
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