ANN-based Alzheimer's disease classification from bag of words

Klumpp P, Fritsch JD, Nöth E (2020)

Publication Type: Conference contribution

Publication year: 2020

Publisher: VDE Verlag GmbH

Pages Range: 341-344

Conference Proceedings Title: Speech Communication - 13th ITG-Fachtagung Sprachkommunikation

Event location: Oldenburg DE

ISBN: 9783800747672

Abstract

Alzheimer's disease (AD) is the most frequent cause of dementia and the patient numbers are increasing within an aging society. Prior research has shown that AD significantly affects the speech signal, and many approaches were published on how to detect AD from only speech or spoken text information. In an earlier work, we have proven the reliability of language models to statistically evaluate transcriptions from AD and healthy control participants. Based on these results, we propose the approach of counting word occurrences in transcriptions, storing them in a bag of words (BoW) vector, and using this vector as an input into an artificial neural network which classifies between AD and healthy state. It could be shown that the new method reached very similar results compared to the language model classifiers, although information about the word order was omitted.

Authors with CRIS profile

Philipp Klumpp Lehrstuhl für Informatik 5 (Mustererkennung) Julian David Fritsch Professur für Informatik (Mustererkennung) Elmar Nöth Professur für Informatik (Mustererkennung)

How to cite

APA:

Klumpp, P., Fritsch, J.D., & Nöth, E. (2020). ANN-based Alzheimer's disease classification from bag of words. In Speech Communication - 13th ITG-Fachtagung Sprachkommunikation (pp. 341-344). Oldenburg, DE: VDE Verlag GmbH.

MLA:

Klumpp, Philipp, Julian David Fritsch, and Elmar Nöth. "ANN-based Alzheimer's disease classification from bag of words." Proceedings of the 13th ITG Conference on Speech Communication, Oldenburg VDE Verlag GmbH, 2020. 341-344.

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