Improving Alzheimer's disease diagnosis with machine learning techniques
There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be
based upon clinical history, neuropsychological and laboratory tests, neuroimaging and …
based upon clinical history, neuropsychological and laboratory tests, neuroimaging and …
Predicting dementia with prefrontal electroencephalography and event-related potential
Objective: To examine whether prefrontal electroencephalography (EEG) can be used for
screening dementia. Methods: We estimated the global cognitive decline using the results of …
screening dementia. Methods: We estimated the global cognitive decline using the results of …
[图书][B] Computational intelligence in biomedical engineering
As in many other fields, biomedical engineers benefit from the use of computational
intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even …
intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even …
An ensemble based data fusion approach for early diagnosis of Alzheimer's disease
As the number of the elderly population affected by Alzheimer's disease (AD) rises rapidly,
the need to find an accurate, inexpensive and non-intrusive diagnostic procedure that can …
the need to find an accurate, inexpensive and non-intrusive diagnostic procedure that can …
Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease
Early diagnosis of Alzheimer's disease (AD) is becoming an increasingly important
healthcare concern. Prior approaches analyzing event-related potentials (ERPs) had …
healthcare concern. Prior approaches analyzing event-related potentials (ERPs) had …
An algebraic method for eye blink artifacts detection in single channel EEG recordings
Single channel EEG systems are very useful in EEG based applications where real time
processing, low computational complexity and low cumbersomeness are critical constrains …
processing, low computational complexity and low cumbersomeness are critical constrains …
Time series for blind biosignal classification model
Biosignals such as electrocardiograms (ECG), electroencephalograms (EEG), and
electromyograms (EMG), are important noninvasive measurements useful for making …
electromyograms (EMG), are important noninvasive measurements useful for making …
Blind biosignal classification framework based on DTW algorithm
Biosignal is a noninvasive measurement of the status of internal organism, such as
electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG), etc …
electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG), etc …
[PDF][PDF] Support vector machines in the diagnosis of Alzheimer's disease
The diagnosis of Alzheimer's disease involves neurological examinations, among which one
can mention the electroencephalogram (EEG). The waveforms collected by the EEG scalp …
can mention the electroencephalogram (EEG). The waveforms collected by the EEG scalp …
Alzheimer's Disease Diagnosis Using Brain Signals and Artificial Neural Networks
E Mazrooei Rad - The Neuroscience Journal of Shefaye Khatam, 2023 - shefayekhatam.ir
Introduction: An unexpected number of people are at risk of Alzheimer's disease. Therefore,
efforts to find effective preventive measures require to be intensified. Materials and Methods …
efforts to find effective preventive measures require to be intensified. Materials and Methods …