This paper focuses on selecting features that can best represent the pathophysiology of Parkinson’s disease (PD) dysarthria. PD dysarthria has often been the subject of feature selection and classification experiments, but rarely have the selected features been attempted to be matched to the pathophysiology of PD dysarthria. PD dysarthria manifests through changes in control of a person’s speech production muscles and affects respiration, articulation, resonance, and laryngeal properties, resulting in speech characteristics such as short phrases separated by pauses, reduced speed for non-repetitive syllables or supernormal speed of repetitive syllables, reduced resonance, irregular vowel generation, etc. Articulation, phonation, diadochokinesis (DDK) rhythm, and Empirical Mode Decomposition (EMD) features were extracted from the DDK and sustained /a/ recordings of the Spanish GITA Corpus. These recordings were captured from 50 healthy (HC) and 50 PD subjects. A two-stage filter-wrapper feature selection process was applied to reduce the number of features from 3,534 to 15. These 15 features mainly represent the instability of the voice and rhythm. SVM, Random Forest and Naive Bayes were used to test the discriminative power of the selected features. The results showed that these sustained /a/ and /pa-ta-ka/ stability features could successfully discriminate PD from HC with 70% accuracy.