Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SNAMS.2018.8554886 | 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS) |
Keywords | Field | DocType |
Bipolar Disorder,Deep Learning,Bidirectional Long Short-Term Memory,Feature Fusion | Mood,Mel-frequency cepstrum,Mania,Sliding window protocol,Bipolar disorder,Computer science,Feature extraction,Speech recognition,Concatenation,Artificial intelligence,Deep learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-9589-0 | 1 | 0.35 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maad Ebrahim | 1 | 1 | 0.35 |
| Mahmoud Al-Ayyoub | 2 | 730 | 63.41 |
| Mohammad A. Alsmirat | 3 | 130 | 16.98 |