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The study utilizes investigational software, the SPARK Test, with an FDA-cleared electroencephalography (EEG) amplifier and EEG cap to collect and patient EEG data.
Full description
The aim of this study is to collect data to support development of an algorithm to determine whether applying machine-learning techniques to eyes open/eyes closed resting-state electroencephalography (EEG) can characterize patient's cognitive status and detect the presence or absence of AD on the basis of the patient's EEG.
Enrollment
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Inclusion criteria
Exclusion criteria
Unable to remain still for up to 30 minutes during EEG data recording
Subjects currently on and unable to wash out concomitant medications, including: 1) opiates; 2) benzodiazepines and nonbenzodiazepine hypnotics; 3) sedative antihistamines; 4) tricyclic anti-depressants; 5) skeletal muscle relaxants; 6) antiepileptics; 7) antipsychotics; 8) antimanic agents; 9) THC; 10) anticholinergics
Previous history of craniotomy
Medical or psychiatric illness that would interfere with study participation
History of epilepsy or chronic seizure disorder
Presence of non-dental metal in head
Currently experiencing a skin disease on scalp that would affect electrode contacts
Subject meets at least one of the following criteria:
Substance Use Disorder, including Alcohol
Primary purpose
Allocation
Interventional model
Masking
185 participants in 1 patient group
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Central trial contact
Che Lucero; Marinela Gombosev
Data sourced from clinicaltrials.gov
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