Voice analysis for diagnosis and severity monitoring of Parkinson’s disease
Creators: name: PUZHAVAKKATHUMADOM VISWANATHAN, Rekha
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400305
subject: Biomedical instrumentation

description: In Parkinson's disease (PD) the motor and non-motor actions like speech, facial expression, blinking, swallowing, gait, sleep and emotions are affected due to dopamine depletion in some regions of the brain. PD assessment methods such as unified Parkinson's disease rating scale (UPDRS) and Hoehn and Yahr (H &amp; Y) perform well in advanced stages of the disease.  However, PD assessment using the above methods becomes more challenging in the early stages of the disease as a variety of symptoms in this stage overlap with other neurological conditions. Speech impairments are well recognised in PD patients and are believed to be one of the earliest symptoms of PD. In previous studies, nonlinear-dynamics voice measures have been applied to differentiate PD and controls based on the chaotic or complex nature of PD voices. However, these vocal measures are ineffective in differentiating early-stage PD and control subjects. Moreover, it has been reported that cognitively challenging tasks increase motor impairment in PD patients. Yet, there exists a gap in the knowledge on the voice response of PD patients to cognitively challenging speech tasks; addressing this gap could improve the assessment of motor speech impairments.<br><br>
The study has proposed novel and efficient vocal features for the early screening of PD subjects by studying the phonetic signal complexity, vowels and consonant phonatory signals and single word tasks with cognitive loading. The thesis has also examined the effect of Levodopa medication on the voices of PD patients. Literature has suggested that aging and disease can change the complexity of biological systems. From the above evidence, it was hypothesised that the voice of PD patients will have reduced complexity when compared with control subjects, indicative of reduced vocal dynamics. It was also hypothesised that, because of the reduced complexity in the voice of PD, the information shared (or similarity) between them will be greater than the information shared between the voices of controls. Two complexity measures were investigated-fractal dimension (FD) and normalised mutual information (NMI)-to test these hypotheses. This work has investigated three phonetic signals /a/, /u/ and /m/ for the computation of FD and NMI. <br><br>
The results showed that PD had reduced complexity in phonetic sound signals compared with control subjects. It was also observed that NMI was significantly higher within the PD compared with the control group. It was also found that control-PD pairs had higher NMI compared with control-control pairs. FD and NMI features also showed high sensitivity in classifying PD and controls. Therefore, it is suggested that using NMI and FD of voices independently or along with other front-line vocal features could lead to better population screening of speech pathologies.<br><br>
The efficiency of vocal features from sustained vowels and consonants in differentiating PD and controls was tested as a part of the study. The results showed that the features from /m/ were able to achieve a classification accuracy of 96% while /a/ and /u/ achieved 84% and 70% accuracy respectively. The vocal features were also employed to automatically estimate the motor UPDRS score of the subjects using various regression models and it was observed that the features from /m/ obtained mean absolute error of 3.52 while that for /a/ and /u/ were 5.56 and 7.88 respectively. The study thus demonstrated the efficiency of features from /m/ in differentiating PD and control subjects.<br><br>
Few studies have incorporated attention-demanding tasks to study the effects of cognitive loading in PD voices. Literature has shown that a visuo-manual tracking task affects volumetric and temporal measures of speech motor control. However, the effect of cognitive loading on acoustic voice measures has not been analysed in a PD population. This research has employed cognitive loading-based speech tasks for the identification of PD subjects and to study the effect of cognitive loading in PD voices. Known vocal features were extracted from two levels cognitive loading tasks; the congruent Stroop word test and the incongruent Stroop word test. It was hypothesised that as cognitive loading increases, vocal features show more fluctuation in PD subjects than the control subjects. The results showed that cognitive loading made voices of controls less noisy whereas the voices of PD became more aperiodic. It was also shown that cognitive loading produced different effects on the vocal features of male and female subjects. This work has shown that an increase in speech task difficulty can better differentiate PD and control subjects.<br><br>
Finally, the effect of Levodopa medication on the voices of PD was examined. The vocal features from the phonation task and cognitive loading-based tasks were compared before and after medication. The results demonstrated no changes in the complexity of signals before and after medication. The known vocal features extracted from phonation tasks also did not show any change. For the cognitive loading-based tasks, there was a slight improvement in the vocal features of PD subjects though not remarkable. This indicates that the Levodopa medication did not tend to improve the voice in PD subjects. The overall results of this thesis would find applications in aiding clinicians to identify early onset of PD through simple, non-invasive speech tests and for better clinical decision making in PD subjects.