Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines


Share/Save/Bookmark

Sluis, Frans van der and Dijkstra, Ton and Broek, Egon L. van den (2012) Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines. In: International Conference on Health Informatics, HealthInf 2012, 1-4 February 2012, Vilamoura, Algarve, Portugal (pp. pp. 493-498).

[img] PDF
Restricted to UT campus only
: Request a copy
105kB
Abstract:This study explores the feasibility of sensitive machines; that is, machines with empathic abilities, at least to some extent. A signal processing and machine learning pipeline is presented that is used to analyze data from two studies in which 25 Post-Traumatic Stress Disorder (PTSD) patients participated. The feasibility of speech as a stress detector was validated in a clinical setting, using the Subjective Unit of Distress (SUD). 13 statistical parameters were derived from five speech features, namely: amplitude, zero crossings, power, high-frequency power, and pitch. To achieve a low dimensional representation, a subset of 28 parameters was selected and, subsequently, compressed into 11 principal components (PC). Using a Multi-Layer Perceptron neural network (MLP), the set of 11 PC were mapped upon 9 distinct quantizations of the SUD. The MLP was able to discriminate between 2 stress levels with 82.4% accuracy and up to 10 stress levels with 36.3% accuracy. With stress baptized as being the black death of the 21st century, this work can be conceived as an important step towards computer aided mental health care.
Item Type:Conference or Workshop Item
Copyright:© 2012 SciTePress
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:http://purl.utwente.nl/publications/79668
Related URL:http://www.healthinf.biostec.org/
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page