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Personalised Speech-Based PTSD Prediction Using Weighted-Instance Learning

MCML Authors

Link to Profile Björn Schuller

Björn Schuller

Prof. Dr.

Principal Investigator

Health Informatics

Abstract

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples’ daily life and can affect their mental, physical, or social wellbeing, which is why a timely and professional treatment is required. In this paper, we propose a personalised speech-based PTSD prediction approach using a newly collected dataset which consists of 15 participants, including speech recordings from people with PTSD and healthy controls. In addition, the dataset includes data before and after a clinical intervention so that the prediction can be analysed at different points in time. In our experiments, we demonstrate the superiority of the personalised approach, achieving a best area under the ROC curve (AUC) of 82% and a best relative improvement of 7% points compared to the non-personalised model.

inproceedings

EMBC 2024

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Orlando, FL, USA, Jul 15-19, 2024.

Authors

A. Kathan • S. Amiriparian • A. Triantafyllopoulos • A. Gebhard • S. Milkus • J. Hohmann • P. Muderlak • J. Schottdorf • R. Musil • B. W. Schuller

Links

DOI

Research Area

 B3 | Multimodal Perception

BibTeXKey: KAT+24

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