Morphological Neural Networks for Parkinson Detection through Speech Signals

This paper presents the implementation of morpho- logical neural networks in the identification of subjects with Par- kinson’s disease. We use bio-markers from “Oxford Parkinson’s Disease Screening”, which contains a total of 195 sustained voice donations with 32 patients male and female, of which 24 o them were diagnosed with Parkinson’s disease and eight correspond to people healthy. Although different algorithms of machine learning have treated this problem, the use of dendrites morphological neu- ral networks proved to have an excellent ability to identify subjects with Parkinson; the stochastic gradient descent learning algo- rithm obtained an accuracy of 94.74%, a precisión of 91.32%, a sensitivity of 86.98% and a specificity of 97.28%. These results are better than other sophisticated and proposed algorithms show in the results.