LSTM Siamese Network for Parkinsonxe2x80x99s Disease Detection from Speech Parkinsonxe2x80x99s disease (PD) is a neurodegenerative disorder that severely affects motor functions. Symptoms include dysarthria and this fact has been the basis for PD detection from speech in several works. Machine learningbased technologies have made significant strides in automatic speech recognition, but their use is fairly limited for clinical diagnosis due to the lack of large corpora containing parkinsonian speech.In this work, we propose a twostep strategy to use machine learning methods for PD detection. In the first step, we use Long ShortTerm Memory (LSTM)based siamese networks to learn feature representations that highlight the information related to speech articulation and prosody relevant for PD detection. Siamese networks are trained on data pairs employing a Spanish corpus containing 52 patients and 56 control subjects. In the second step, we train a classifier to make decisions about the presence or absence of PD employing the features provided by the LSTM networks.We achieve an EER of 1.9% in the detection by combining the scores of different textdependent models. Preliminary experiments show the efficacy of the proposed method and prove the usefulness of LSTM for PD detection from speech.

A Deep ConvolutionalRecurrent Neural Network Architecture for Parkinsonxe2x80x99s Disease EEG Classification Parkinsonxe2x80x99s disease (PD), characterized by slowness of movement, tremor and rigidity, is one of the most prevalent neurodegenerative disorders. Recent studies have demonstrated that abnormal neural oscillations within and between multiple brain regions play a critical role in the motor symptoms through invasive neural recordings. Progressions have been also made in EEG studies to use features in cortical oscillations recorded noninvasively as a diagnostic tool for PD. However, it is still challenging to effectively use EEG recordings for PD diagnosis. In this work, we design a novel deep learning framework for PD EEG classification. Specifically, the convolutional neural network (CNN) and the recurrent neural network (RNN) with long shortterm memory (LSTM) cells are exploited in our framework. First, we design two 1DCNN layers to derive features to represent spatial (topological) relationships across EEG channels. Then, we apply LSTM on the spatial features from the CNN to further improve its performance. Finally, we validate our model on the PD classification on resting EEG recorded from 20 PD and 21 healthy subjects. Our method achieves accuracy of 96.9%, precision of 100%, and recall of 93.4% for differentiating PD from healthy controls and outperforms the stateoftheart PD EEG classification results in the deep learning literature.