Author
Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk
Publication Date
2014
Publication Place
-
IEEE
Subject
Diseases, Feature extraction, Medical signal processing, Natural language processing, Patient diagnosis, Patient monitoring, Speech processing, Speech recognition
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2168-2194
Record ID
86f1b116-344d-458e-8a73-33e73e0201a9
Library Location
Electrical & Electronics Engineering
Date
2014
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
Automatic monitoring of the patients with Alzheimer's disease and diagnosis of the disease in early stages can have a significant impact on the society. Here, we investigate an automatic diagnosis approach through the use of features derived from transcriptions of conversations with the subjects. As opposed to standard tests that are mostly focused on memory recall, spontaneous conversations are carried with the subjects in informal settings. Features extracted from the transcriptions of the conversations could discriminate between healthy people and patients with high reliability. Although the results are preliminary and patients were in later stages of Alzheimer's disease, results indicate the potential use of the proposed natural language based features in the early stages of the disease also. Moreover, the data collection process employed here can be done inexpensively by call center agents in a real-life application using automatic speech recognition systems (ASR) which are known to have very high accuracies in recent years. Thus, the investigated features hold the potential to make it low-cost and convenient to diagnose the disease and monitor the diagnosed patients over time.
DOI
10.1109/BHI.2014.6864431
