@inproceedings{e7cf72d876ea43d09f8c10c40dc9b7c0,
title = "Determine Bipolar Disorder Level from Patient Interviews Using Bi-LSTM and Feature Fusion",
abstract = "Patients with Bipolar Disorder (BD) suffer from a brain disorder that cause them to change mood without reasons and prevent them from performing ordinary daily tasks. In this work, we classify patients with BD into one of its three levels: remission, hypo-mania, and mania, based solely on audio-visual recordings of structured interviews with these patients by the use of different deep learning techniques coupled with feature fusion and concatenation techniques along with a simple sliding window procedure. The results of our approach are promising and open up the door for many contributions and improvements in the future.",
keywords = "Bidirectional Long Short-Term Memory, Bipolar Disorder, Deep Learning, Feature Fusion",
author = "Maad Ebrahim and Mahmoud Al-Ayyoub and Mohammad Alsmirat",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018 ; Conference date: 15-10-2018 Through 18-10-2018",
year = "2018",
month = nov,
day = "30",
doi = "10.1109/SNAMS.2018.8554886",
language = "English",
series = "2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "182--189",
booktitle = "2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018",
address = "United States",
}