Emotional State Modeling for the Assessment of Depression in Parkinson’s Disease

Abstract

Parkinson’s disease (PD) results from the degeneration of dopamine in the substantia nigra, which plays a role in motor control, mood, and cognitive functions. Some processes in the brain of a PD patient can be overlapped with non-motor functions, where some of the same brain circuitry that is related to mood regulation is also affected. Commonly, most patients experience motor symptoms such as speech impairments, bradykinesia, or resting tremor; while non-motor symptoms such as sleep disorders or depression may also appear in PD. Depression is one of the most common non-motor symptoms developed by patients and is also associated with the rapid progression of motor impairments. This study proposes the use of the “Pleasure, Arousal, and Dominance Emotional State Model” (PAD) to capture similar aspects related to mood and affective states in PD patients. The PAD representation is commonly used to quantify and represent emotions in a multidimensional space. Acoustic information is used as input to feed a deep learning model based on convolutional and recurrent neural networks, which are trained to model the PAD representation. The proposed approach consists of performing transfer knowledge from the PAD model for the classification and the assessment of depression in PD. F1-scores of up to 0.69 are obtained for the classification of PD patients vs. healthy controls and of up to 0.85 for the discrimination between depressive PD vs. non-depressive PD patients, which confirms that there is information embedded in the PAD model that can be used to detect depression in PD.