Kohonen-Based Topological Clustering as an Amplifier for Multi-Class Classification for Parkinson's Disease

Alex Frid, Larry M. Manevitz, Ohad Mosafi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Classifying the degree of Parkinson's disease is an important clinical necessity. Nonetheless, current methodology requires manual (and subjective) evaluation by a trained clinical expert. Recently, Machine Learning tools have been developed that can produce a classification of the presence of PD directly from the speech signal in an automated and objective fashion. However, these methods were not sufficient for the classification of the degree of the disease. In this work, we show how to apply and leverage topological information on the both the label space and the feature space of the speech signal in order to solve this problem.We address the problem by performing topological clustering (using a version of the Kohonen Self Organizing Map algorithm) of the feature space and then optimizing separate multi-class classifiers on each cluster.Using these methods, we can reliably train our system to classify new speech signal data to more than the 70% level on a 7 degree classification (where random level is 14%) which is close to the obtainable accuracy on the simple 2 class classification.

Original languageEnglish
Title of host publication2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663783
DOIs
StatePublished - 20 Feb 2019
Externally publishedYes
Event2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/TerritoryIsrael
CityEilat
Period12/12/1814/12/18

Bibliographical note

Funding Information:
This work was partially supported by a grant for computational equipment by the Caesarea Rothschild Institute and by a Hardware Grant by NVIDIA Corporation to the Neurocomputation Laboratory.

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Kohonen
  • Multi-Class Classification
  • Parkinson's disease
  • SOM
  • Speech Signal Analysis
  • Topological Clustering

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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