TY - GEN
T1 - Personalised Speech-Based PTSD Prediction Using Weighted-Instance Learning
AU - Kathan, Alexander
AU - Amiriparian, Shahin
AU - Triantafyllopoulos, Andreas
AU - Gebhard, Alexander
AU - Milkus, Sabrina
AU - Hohmann, Jonas
AU - Muderlak, Pauline
AU - Schottdorf, Jürgen
AU - Musil, Richard
AU - Schuller, Björn W.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85215005179&partnerID=8YFLogxK
U2 - 10.1109/EMBC53108.2024.10782220
DO - 10.1109/EMBC53108.2024.10782220
M3 - Conference contribution
AN - SCOPUS:85215005179
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Y2 - 15 July 2024 through 19 July 2024
ER -