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A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals
(IEEE (Institute of Electrical and Electronics Engineers), 2020)
Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection ...
Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns
(Institute of Physics Publishing, 2020)
Physical action recognition is a hot topic in human-machine interactions. It has potential uses in helping disabled people and in various robotic applications. Electromyography (EMG) signals measure the electrical activity ...
A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings
(Institute of Electrical and Electronics Engineers Inc., 2022)
Machine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography ...
Electrocardiogram beat classification using deep convolutional neural network techniques
(Institute of Physics Publishing, 2020)
The electrocardiogram (ECG) is a useful method which enables the monitoring of various cardiac conditions, such as arrhythmia and heart rate variability (HRV). ECG beats help to determine various heart failures such as ...
Two-stepped majority voting for efficient EEG-based emotion classification
(Springer, 2020)
In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and ...