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

Frans van der Sluis; Ton Dijkstra; Egon van den Broek

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

Frans van der Sluis
, Ton Dijkstra
, Egon van den Broek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

65 Downloads (Pure)

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.

Original language	Undefined
Title of host publication	Proceedings of the International Conference on Health Informatics, HealthInf 2012
Editors	E. Conchon, C. Correia, A. Fred, H. Gamboa
Place of Publication	Portugal
Publisher	SCITEPRESS
Pages	493-498
Number of pages	7
ISBN (Print)	978-989-8425-88-1
Publication status	Published - 1 Feb 2012
Event	International Conference on Health Informatics, HEALTHINF 2012 - Vilamoura, Algarve, Portugal Duration: 1 Feb 2012 → 4 Feb 2012

Publication series

Name
Publisher	SciTePress - Science and Technology Publications

Conference

Conference	International Conference on Health Informatics, HEALTHINF 2012
Period	1/02/12 → 4/02/12
Other	1-4 February 2012

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

METIS-285126
IR-79668
Stress
Speech
Validation
HMI-HF: Human Factors
Mental healthcare
Computer Aided Diagnostics (CAD)
EWI-21504
HMI-SLT: Speech and Language Technology
artificial neural network

Access to Document

VanderSluis12-Computer_Aided_Diagnosis_for_mental_health_care

http://dblp.uni-trier.de/db/conf/biostec/healthinf2012.html#SluisDB12

Cite this

van der Sluis, F., Dijkstra, T., & van den Broek, E. (2012). Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines. In E. Conchon, C. Correia, A. Fred, & H. Gamboa (Eds.), Proceedings of the International Conference on Health Informatics, HealthInf 2012 (pp. 493-498). SCITEPRESS. http://dblp.uni-trier.de/db/conf/biostec/healthinf2012.html#SluisDB12

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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.",

keywords = "METIS-285126, IR-79668, Stress, Speech, Validation, HMI-HF: Human Factors, Mental healthcare, Computer Aided Diagnostics (CAD), EWI-21504, HMI-SLT: Speech and Language Technology, artificial neural network",

author = "\{van der Sluis\}, Frans and Ton Dijkstra and \{van den Broek\}, Egon",

year = "2012",

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van der Sluis, F, Dijkstra, T & van den Broek, E 2012, Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines. in E Conchon, C Correia, A Fred & H Gamboa (eds), Proceedings of the International Conference on Health Informatics, HealthInf 2012. SCITEPRESS, Portugal, pp. 493-498, International Conference on Health Informatics, HEALTHINF 2012, 1/02/12. <http://dblp.uni-trier.de/db/conf/biostec/healthinf2012.html#SluisDB12>

Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines. / van der Sluis, Frans; Dijkstra, Ton; van den Broek, Egon.
Proceedings of the International Conference on Health Informatics, HealthInf 2012. ed. / E. Conchon; C. Correia; A. Fred; H. Gamboa. Portugal: SCITEPRESS, 2012. p. 493-498.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - 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.

AB - 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.

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KW - EWI-21504

KW - HMI-SLT: Speech and Language Technology

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BT - Proceedings of the International Conference on Health Informatics, HealthInf 2012

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A2 - Correia, C.

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