Electroencephalography (EEG) Signal Processing

• Status: Unknown status
• Trial end date: 2013-10-01
• Target enrollment: 0
• Participant gender: All

Summary

Current methods of choosing treatment for major depressive disorder (MDD) are inefficient. The Strategic Treatment to Achieve Remission of Depression (STAR*D) Trial revealed that only about 1/3 of patients treated with antidepressant drugs will go into remission with the first medication chosen. We hypothesize that pattern recognition software using Machine Learning methods can accurately predict response to a variety of antidepressant medications (ADM) or cognitive behavior therapy (CBT) after training using pre-treatment demographic, clinical, laboratory or electroencephalographic (EEG) data. These algorithms might assist the clinician to chose, for any given patient, an antidepressant treatment option with greater probability of favourable response than is achievable using current best practise methods.

• Accepts Healthy Volunteers?: No

Details

• Lead Sponsor: St. Joseph's Healthcare Hamilton
• Treatments: Antidepressive Agents; Bupropion; Citalopram; Dexetimide; Duloxetine Hydrochloride; Venlafaxine Hydrochloride

Criteria

Inclusion Criteria:

• Clients with Major Depression

• Males and Females ages 18 - 70

Exclusion Criteria:

• Clients who have known neurological problems

• Clients with a history of severe head injury

• Clients with strong thoughts of suicide

• Clients who have had ECT or Cognitive Behavior Therapy within 6 months

• Females who are sexually active and are not on adequate birth control