Selective review of offline change point detection methods
This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …
change points in multivariate time series. A general yet structuring methodological strategy …
Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts
PM Rossini, R Di Iorio, F Vecchio, M Anfossi… - Clinical …, 2020 - Elsevier
Alzheimer's disease (AD) is the most common neurodegenerative disease among the
elderly with a progressive decline in cognitive function significantly affecting quality of life …
elderly with a progressive decline in cognitive function significantly affecting quality of life …
An open-source, high-performance tool for automated sleep staging
The clinical and societal measurement of human sleep has increased exponentially in
recent years. However, unlike other fields of medical analysis that have become highly …
recent years. However, unlike other fields of medical analysis that have become highly …
Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference
This paper investigates the problem of detection and classification of unmanned aerial
vehicles (UAVs) in the presence of wireless interference signals using a passive radio …
vehicles (UAVs) in the presence of wireless interference signals using a passive radio …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
A comparative review on sleep stage classification methods in patients and healthy individuals
Background and objective: Proper scoring of sleep stages can give clinical information on
diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep …
diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep …
Comparison of different feature extraction methods for EEG-based emotion recognition
EEG-based emotion recognition is a challenging and active research area in affective
computing. We used three-dimensional (arousal, valence and dominance) model of emotion …
computing. We used three-dimensional (arousal, valence and dominance) model of emotion …
Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations
There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …
neural activity measured by electroencephalography (EEG) signals. On top of revealing …
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
Diagnosing depression in the early curable stages is very important and may even save the
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …
UAV detection and identification based on WiFi signal and RF fingerprint
The security threats caused by the popularity of Unmanned Aerial Vehicles (UAVs) have
received much attention. In this paper, a UAV detection and identification system based on …
received much attention. In this paper, a UAV detection and identification system based on …