xyrusgallito/Depression_detection
Deep Learning Approach on Automatic Classification of Depression using ECG and EDA physiological signals.
This project helps clinicians, researchers, and mental health professionals automatically identify potential signs of depression. It takes physiological signal data, specifically electrocardiogram (ECG) and electrodermal activity (EDA) readings, and processes them to output a classification indicating the likelihood of depression. This allows for an objective, data-driven approach to assist in mental health assessments.
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Use this if you are a mental health practitioner or researcher working with physiological data and need an automated system to help screen for or identify depression markers.
Not ideal if you primarily work with qualitative assessments or do not have access to ECG and EDA physiological signal data for your clients.
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Jun 15, 2023
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