  Deep-Brain Stimulation (DBS) is a therapy used in conjunction with medication
to help alleviate the motor symptoms of Parkinson's Disease (PD). However, the
monitoring and adjustment of DBS settings is tedious and expensive, requiring
long programming appointments every few months. We investigated the possible
correlation between PD motor score severity and digitally extracted patient
voice features to potentially aid clinicians in their monitoring and treatment
of PD with DBS, and eventually enable a closed-loop DBS system. 5 DBS PD
patients were enrolled. Voice samples were collected for various voice tasks
(single phoneme vocalization, free speech task, sentence reading task, counting
backward task, categorical fluency task) for DBS ON and OFF states. Motor
scores per the Unified Parkinson's Disease Rating Scale (UPDRS) were also
collected for DBS ON and OFF states. Voice samples were then analyzed to
extract voice features using publicly available voice feature library sets, and
statistically compared for DBS ON and OFF. Of the feature categories explored
(Acoustic, Prosodic, Linguistic) 6 features from the GeMAPS feature set for
acoustic features demonstrated significant differences with DBS ON and OFF
(p<0.05). Prosodic features such as pause length/percentage were found to be
negatively correlated with increased motor symptom severity. Non-significant
differences were found for linguistic features. These findings provide
preliminary evidence for acoustic and prosodic speech features to act as
potential biomarkers for PD disease severity in DBS patients. We hope to
explore further by expanding our data set, identifying other features, applying
machine learning models, and working towards a closed-loop DBS system that can
auto-tune itself based on changes in a patient's voice.
