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Volume 3 | Issue 11

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Paper Title: Machine Learning Approach for Improved PTSD Diagnosis
Authors Name: P.Venkata Sai , Kahol Gaurav B.U , R.Rajesh Kumar
Unique Id: IJRTI1810019
Published In: Volume 3 Issue 10, October-2018
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.
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Cite Article: "Machine Learning Approach for Improved PTSD Diagnosis", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 10, page no.104 - 112, October-2018, Available :http://www.ijrti.org/papers/IJRTI1810019.pdf
Downloads: 000648
Publication Details: Published Paper ID: IJRTI1810019
Registration ID:180511
Published In: Volume 3 Issue 10, October-2018
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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