Cognitive Impairment Analysis of Myotonic Dystrophy via Weakly Supervised Classification of Neuropsychological Features

Tahereh Kamali, Gayle K. Deutsch, Katharine A. Hagerman, Dana Parker, John W. Day, Jacinda B. Sampson, Jeffrey R. Wozniak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The myotonic dystrophies (DM1 and DM2) are dominantly inherited disorders that cause pathological changes throughout the body. Many individuals with DM experience cognitive, behavioral and other functional central nervous system effects that impact their quality of life. The extent of psychological impairment that will develop in each patient is variable and unpredictable. Hence, it is difficult to get strong supervision information like fully ground truth labels for all cognitive involvement patterns. This study is to assess cognitive involvement among healthy controls and patients with DM. The DM cognitive impairment pattern observation is modeled in a weakly supervised setting and supervision information is used to transform the input feature space to a more discriminative representation suitable for pattern observation. This study incorporated results from 59 adults with DM and 92 control subjects. The developed system categorized the neuropsychological testing data into five cognitive clusters. The quality of the obtained clustering solution was assessed using an internal validity metric. The experimental results show that the proposed algorithm can discover interesting patterns and useful information from neuropsychological data, which will be be crucial in planning clinical trials and monitoring clinical performance. The proposed system resulted in an average classification accuracy of 88%, which is very promising considering the unique challenges present in this population.

Original languageEnglish (US)
Title of host publication44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4377-4382
Number of pages6
Volume2022
ISBN (Electronic)9781728127828
DOIs
StatePublished - 2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom
Duration: Jul 11 2022Jul 15 2022

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2022-July
ISSN (Print)1557-170X

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period7/11/227/15/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Cognitive Impairment Analysis
  • Imbalanced Dataset
  • Myotonic Dystrophy
  • Neighborhood Distance Entropy
  • Random Forests
  • Support Vector Machines
  • Weakly Supervised Learning
  • Humans
  • Cognitive Dysfunction/diagnosis
  • Neuropsychological Tests
  • Myotonic Dystrophy/diagnosis
  • Quality of Life
  • Adult
  • Cluster Analysis

PubMed: MeSH publication types

  • Journal Article

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