Name
Title | ||
|---|---|---|
A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis |
Abstract | ||
|---|---|---|
Post-traumatic stress disorder (PTSD) is a traumatic-stressor related disorder developed by exposure to a traumatic or adverse environmental event that caused serious harm or injury. Structured interview is the only widely accepted clinical practice for PTSD diagnosis but suffers from several limitations including the stigma associated with the disease. Diagnosis of PTSD patients by analyzing speech signals has been investigated as an alternative since recent years, where speech signals are processed to extract frequency features and these features are then fed into a classification model for PTSD diagnosis. In this paper, we developed a deep belief network (DBN) model combined with a transfer learning (TL) strategy for PTSD diagnosis. We computed three categories of speech features and utilized the DBN model to fuse these features. The TL strategy was utilized to transfer knowledge learned from a large speech recognition database, TIMIT, for PTSD detection where PTSD patient data is difficult to collect. We evaluated the proposed methods on two PTSD speech databases, each of which consists of audio recordings from 26 patients. We compared the proposed methods with other popular methods and showed that the state-of-the-art support vector machine (SVM) classifier only achieved an accuracy of 57.68%, and TL strategy boosted the performance of the DBN from 61.53% to 74.99%. Altogether, our method provides a pragmatic and promising tool for PTSD diagnosis. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDM.2017.10 | 2017 IEEE International Conference on Data Mining (ICDM) |
Keywords | Field | DocType |
Post-traumatic stress disorder,Deep learning,Transfer learning,Deep belief network | TIMIT,Mel-frequency cepstrum,Structured interview,Traumatic stress,Computer science,Transfer of learning,Deep belief network,Support vector machine,Speech recognition,Artificial intelligence,Deep learning,Machine learning | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-5386-2449-4 | 5 |
PageRank | References | Authors |
0.45 | 22 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Banerjee, D. | 1 | 13 | 1.67 |
| Kazi Islam | 2 | 5 | 0.45 |
| Gang Mei | 3 | 7 | 2.17 |
| Lemin Xiao | 4 | 9 | 3.50 |
| Guangfan Zhang | 5 | 39 | 4.64 |
| Roger Xu | 6 | 111 | 14.71 |
| Shuiwang Ji | 7 | 2579 | 122.25 |
| Jiang Li | 8 | 251 | 27.28 |