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Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques
This work shows that early diagnosis of Parkinson's disease is possible solely from the voice signal, and conjecture that such systems can be extended to monitoring and classifying additional neurological diseases and speech pathologies. Expand
An SVM based algorithm for analysis and discrimination of dyslexic readers from regular readers using ERPs
Dyslexia is a learning disability that impairs a person's ability to decode words accurately and fluently. This deficit can manifest itself in the language-related domain as difficulties inExpand
Fusing enacted and expected mimicry generates a winning strategy that promotes the evolution of cooperation
It is suggested that reducing conflict intensities among human populations necessitates instigation of social initiatives that increase the perception of similarity among opponents and efficient lowering of the similarity threshold of the interaction, the minimal level of similarity that makes cooperation advisable. Expand
Features and Machine Learning for Correlating and Classifying between Brain Areas and Dyslexia
We develop a method that is based on processing gathered Event Related Potentials (ERP) signals and the use of machine learning technique for multivariate analysis (i.e. classification) that we applyExpand
Kohonen-Based Topological Clustering as an Amplifier for Multi-Class Classification for Parkinson’s Disease
This work addresses the problem of classification of the degree of Parkinson’s disease by performing topological clustering of the feature space and then optimizing separate multi-class classifiers on each cluster using a version of the Kohonen Self Organizing Map algorithm. Expand
Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor
Parkinson's Disease (PD) is a degenerative disease of the central nervous system with a profound effect on the motor system. Symptoms include slowness of movement, rigidity of motion and in someExpand
Diagnosis of Parkinson's disease from continuous speech using deep convolutional networks without manual selection of features
This work focuses on automating the process of diagnosis from continuous native speech by removing the necessity of a trained personal from the diagnosis process by using an adaptation of Convolutional Neural Network architecture for one-dimensional signal processing on a relatively small training set. Expand
Spectral and textural features for automatic classification of fricatives
Two dimensionality reduction algorithms, namely, t-distributed Stochastic Neighbor Embedding and Sequential Forward Floating Selection were used to obtain a compact representation of the data and it is shown that representing the data by a feature vector with as few as 3 dimensions, yields a classification rate of almost 90% which outperforms most of the results obtained in previous studies. Expand
Acoustic-phonetic analysis of fricatives for classification using SVM based algorithm
An effective algorithm for classification of one group of phonemes, namely the unvoiced fricatives, which are characterized by a relatively large amount of spectral energy in the high frequency range is presented. Expand
Analyzing cognitive processes from complex neuro-physiologically based data: some lessons
  • A. Frid, L. Manevitz
  • Computer Science
  • Annals of Mathematics and Artificial Intelligence
  • 1 December 2020
This paper describes the experience working on several examples at the edge of capabilities of machine learning systems and describes the various and variant methodologies needed to overcome these sort of challenges. Expand