TY - GEN
T1 - Analyzing acoustic and prosodic fluctuations in free speech to predict psychosis onset in high-risk youths
AU - Agurto, Carla
AU - Pietrowicz, Mary
AU - Norel, Raquel
AU - Eyigoz, Elif K.
AU - Stanislawski, Emma
AU - Cecchi, Guillermo
AU - Corcoran, Cheryl
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The diagnosis and treatment of psychiatric disorders depends on the analysis of behavior through language by a clinical specialist. This analysis is subjective in nature and could benefit from automated, objective acoustic and linguistic processing methods. This integrated approach would convey a richer representation of patient speech, particularly for expression of emotion. In this work, we explore the potential of acoustic and prosodic metrics to infer clinical variables and predict psychosis, a condition which produces measurable derailment and tangentiality in patient language. To that purpose, we analyzed the recordings of 32 young patients at high risk of developing clinical psychosis. The subjects were evaluated using the Structured Interview for Prodromal Syndromes/Scale of Prodromal Symptoms (SIPS/SOPS) criteria. To analyze the recordings, we examined the variation of different acoustic and prosodic metrics across time. This preliminary analysis shows that these features can infer negative symptom severity ratings (i.e., SIPS-Btotal), obtaining a Pearson correlation of 0.77 for all the subjects after cross-validated evaluation. In addition, these features can predict development of psychosis with high accuracy above 90%, outperforming classification using clinical variables only. This improved predictive power ultimately can help provide early treatment and improve quality of life for those at risk for developing psychosis.
AB - The diagnosis and treatment of psychiatric disorders depends on the analysis of behavior through language by a clinical specialist. This analysis is subjective in nature and could benefit from automated, objective acoustic and linguistic processing methods. This integrated approach would convey a richer representation of patient speech, particularly for expression of emotion. In this work, we explore the potential of acoustic and prosodic metrics to infer clinical variables and predict psychosis, a condition which produces measurable derailment and tangentiality in patient language. To that purpose, we analyzed the recordings of 32 young patients at high risk of developing clinical psychosis. The subjects were evaluated using the Structured Interview for Prodromal Syndromes/Scale of Prodromal Symptoms (SIPS/SOPS) criteria. To analyze the recordings, we examined the variation of different acoustic and prosodic metrics across time. This preliminary analysis shows that these features can infer negative symptom severity ratings (i.e., SIPS-Btotal), obtaining a Pearson correlation of 0.77 for all the subjects after cross-validated evaluation. In addition, these features can predict development of psychosis with high accuracy above 90%, outperforming classification using clinical variables only. This improved predictive power ultimately can help provide early treatment and improve quality of life for those at risk for developing psychosis.
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U2 - 10.1109/EMBC44109.2020.9176841
DO - 10.1109/EMBC44109.2020.9176841
M3 - Conference contribution
C2 - 33019241
AN - SCOPUS:85091038957
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 5575
EP - 5579
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -