Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease.

The recently proposed Parkinson's Disease (PD) telediagnosis systems based on detecting dysphonia achieve very high classification rates in discriminating healthy subjects from PD patients. However, in these studies the data used to construct the classification model contain the speech recordings of both early and late PD patients with different severities of speech impairments resulting in unrealistic results. In a more realistic scenario, an early telediagnosis system is expected to be used in suspicious cases by healthy subjects or early PD patients with mild speech impairment. In this paper, considering the critical importance of early diagnosis in the treatment of the disease, we evaluate the ability of vocal features in early telediagnosis of Parkinson's Disease (PD) using machine learning techniques with a two-step approach. In the first step, using only patient data, we aim to determine the patient group with relatively greater severity of speech impairments using Unified Parkinson's Disease Rating Scale (UPDRS) score as an index of disease progression. For this purpose, we use three supervised and two unsupervised learning techniques. In the second step, we exclude the samples of this group of patients from the dataset, create a new dataset consisting of the samples of PD patients having less severity of speech impairments and healthy subjects, and use three classifiers with various settings to address this binary classification problem. In this classification problem, the highest accuracy of 96.4% and Matthew's Correlation Coefficient of 0.77 is obtained using support vector machines with third-degree polynomial kernel showing that vocal features can be used to build a decision support system for early telediagnosis of PD

Best results obtained with k-NN, SVM and ELM with statistical significance tests.

<p>Best results obtained with k-NN, SVM and ELM with statistical significance tests.</p

Ranking of the vocal features based on their mutual information with UPDRS level discretized according to the determined optimal threshold that can be discriminated by machine learning methods.

<p>Ranking of the vocal features based on their mutual information with UPDRS level discretized according to the determined optimal threshold that can be discriminated by machine learning methods.</p

Absolute difference between the ratio of the number of patients whose UPDRS is below the corresponding threshold to the number of all patients in cluster 1 and cluster 2.

<p>Absolute difference between the ratio of the number of patients whose UPDRS is below the corresponding threshold to the number of all patients in cluster 1 and cluster 2.</p

Statistical parameters of vocal features.

<p>Statistical parameters of vocal features.</p

A summary of results obtained with the best settings of classifiers (left) Matthew's correlation coefficients of the classifiers obtained with their best settings (right) ROC space of the classifiers obtained when UPDRS threshold is set to 15.

<p>A summary of results obtained with the best settings of classifiers (left) Matthew's correlation coefficients of the classifiers obtained with their best settings (right) ROC space of the classifiers obtained when UPDRS threshold is set to 15.</p

Definitions of vocal features.

<p>Definitions of vocal features.</p

(left) Test set classification accuracies and (right) Matthew's correlation coefficients obtained with ELM classifier under various UPDRS threshold values.

<p>(left) Test set classification accuracies and (right) Matthew's correlation coefficients obtained with ELM classifier under various UPDRS threshold values.</p