The i-vector approach is used to model the speech of PD patients with the aim of assessing their condition. Features related to the articulation, phonation, and prosody dimensions of speech were used to train different i-vector extractors. Each i-vector extractor is trained using utterances from both PD patients and healthy controls. The i-vectors of the healthy control (HC) speakers are averaged to form a single i-vector that represents the HC group, i.e., the reference i-vector. A similar process is done to create a reference of the group with PD patients. Then the i-vectors of test speakers are compared to these reference i-vectors using the cosine distance. Three analyses are performed using this distance: classification between PD patients and HC, prediction of the neurological state of PD patients according to the MDS-UPDRS-III scale, and prediction of a modified version of the Frenchay Dysarthria Assessment. The Spearman's correlation between this cosine distance and the MDS-UPDRS-III scale was 0.63. These results show the suitability of this approach to monitor the neurological state of people with Parkinson's Disease.

Three patients with fascioliasis are presented in whom CT demonstrated abscesses and granulomas and permitted control of the disease's evolution after medical treatment.

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Parkinson's Disease (PD) is a neurodegenerative disorder characterized by a variety of motor symptoms. PD patients show several motor deficits, including speech deficits, impaired handwriting, and gait disturbances. In this work we propose a methodology to compute i-vectors extracted from three different bio-signals: speech, handwriting, and gait. These i-vectors are used to classify patients and healthy controls, and to evaluate the neurological state of the patients. Speech i-vectors are extracted from Mel-Frequency Cepstral Coefficients (MFCCs), handwriting i-vectors are extracted from kinematic features, and gait i-vectors are extracted from modified MFCCs computed from inertial sensor signals. Two fusion strategies are tested: concatenating the i-vectors of a subject to form a super-i-vector with information from the three bio-signals and score pooling. The super-i-vector fusion method leads to better classification results (accuracy of 85%) with respect to the separate analysis with each bio-signal.

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