Assessment and Prediction of Negative Symptoms of Schizophrenia from RGB+D Movement Signals

• Main Authors: Chakraborty, Debsubhra, Tahir, Yasir, Yang, Zixu, Maszczyk, Tomasz, Dauwels, Justin, Thalmann, Daniel, Magnenat Thalmann, Nadia, Tan, Bhing-Leet, Lee, Jimmy
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2017
• Subjects: Negative symptoms; Schizophrenia
• Online Access: https://hdl.handle.net/10356/83648; http://hdl.handle.net/10220/43560
• Institution: Nanyang Technological University

Negative symptoms of schizophrenia significantly affect the daily functioning of patients, especially movement and expressive gestures. The diagnosis of such symptoms is often difficult and require the expertise of a trained clinician. Apart from these subjective methods, there is little research on developing objective methods to quantify the symptoms. Therefore, we explore body movement signals as objective measures of negative symptoms. Specifically, we extract the signals from video recordings of patients being interviewed. We analysed the interviews of 69 paid participants (46 patients and 23 healthy controls) in this study. Correlation between movement signals (linear and angular speeds of upper limbs and head, acceleration and gesture angles) and subjective ratings (assigned during same interview) from the NSA-16 scale were calculated. As hypothesized, the movement signals correlated strongly with the movement impairment aspect of the NSA-16 questionnaire. Also, not quite surprisingly, strong correlations were obtained between the movement signals and speech items of NSA-16, indicating lack of associated gestures in patients during speech. These subjective ratings could also be reasonably predicted from the objective signals with an accuracy of 61-78 % using machine-learning algorithms with leave-one-out cross-validation technique. Furthermore, these objective measures can be reliably utilized to distinguish between the patient and healthy groups, as supervised learning methods can classify the two groups with 74-87 % accuracy.