Name
Abstract | ||
|---|---|---|
Properly diagnosing ADC is heavily dependent on the keen judgment of doctors, often together with specialists like psychiatric, brain or neurology experts. It's easy to imagine how difficult it is to determine impairments in mood and behavior since there's no standard or common course of ADC. In one person it may be very mild with periods of varying severity of symptoms. In another it can be abrupt, severe and progressive. Currently, there is no way to tell how a person will progress with ADC. Sometimes symptoms of ADC are overlooked or dismissed by caregivers, which may believe the symptoms are due to advanced HIV disease. In fact, people with advanced disease generally do not have symptoms of ADC but do have fairly normal mental functioning as long as they also have no other neurological problems. At the other end of the spectrum, ADC should be carefully distinguished from severe depression-common among people with HIV that may result in symptoms similar to ADC. ADC occurs more commonly in children with HIV than with adults. It presents similarly and is often more severe and progressive. In this study, potential of the electroencephalograms (EEGs) of a group of patients are analyzed; half of whom had been diagnosed with early ADC. The EEGs is analyzed using multiresolution wavelet analysis techniques, and processed signals will then be used to train a neural network to distinguish the signal that belongs to patients with ADC from those that belong to patients without ADC. Test of cerebrospinal fund (CSF) helps determine if someone has ADC, but they are not conclusive. Mostly they are used to rule out other causes of the symptoms of ADC. Many people but not all with ADC have higher levels of certain proteins or white blood cells in their CSF Preliminary results using signal processing toolbox of matlab have been very encourasing. Neurophysiologists typically look for existence or lack of certain patterns at specific frequencies in the 0 similar to 100 Hz range when analyzing EEGs. These patterns, such as so called alpha waves at 8-13 Hz, beta waves at 14 similar to 30 Hz, theta waves at 4 similar to 7 Hz and delta waves at frequencies below 3.5 Hz have all been linked to various specific neurophysiological activities. |
Year | Venue | Keywords |
|---|---|---|
2005 | METMBS '05: Proceedings of the 2005 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences | AIDs dementia,artificial neural networks,event related potentials,discrete wavelet transforms,latency |
Field | DocType | Citations |
Early detection,Aids-related dementia,Psychiatry,Medicine | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dinesh P. Mital | 1 | 107 | 32.92 |
| Shankar Srinivasan | 2 | 22 | 15.15 |
| Debebe Asefa | 3 | 1 | 1.08 |