How word semantics and phonology affect handwriting of Alzheimer's patients: A machine learning based analysis.
Comput Biol Med
; 169: 107891, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-38181607
ABSTRACT
Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance the best result was obtained on a non-regular, reaching an accuracy close to 90%.
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Base de dados:
MEDLINE
Assunto principal:
Doenças Neurodegenerativas
/
Doença de Alzheimer
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article