Natural language features for detection of Alzheimer's disease in conversational speech

Name: Natural language features for detection of Alzheimer's disease in conversational speech
Author: Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk

İsim	Natural language features for detection of Alzheimer's disease in conversational speech
Yazar	Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk
Basım Tarihi:	2014
Basım Yeri	- IEEE
Konu	Diseases, Feature extraction, Medical signal processing, Natural language processing, Patient diagnosis, Patient monitoring, Speech processing, Speech recognition
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2168-2194
Kayıt Numarası	86f1b116-344d-458e-8a73-33e73e0201a9
Lokasyon	Electrical & Electronics Engineering
Tarih	2014
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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

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Natural language features for detection of Alzheimer's disease in conversational speech

Yazar Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk

Basım Tarihi 2014

Basım Yeri - IEEE

Konu Diseases, Feature extraction, Medical signal processing, Natural language processing, Patient diagnosis, Patient monitoring, Speech processing, Speech recognition

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2168-2194

Kayıt Numarası 86f1b116-344d-458e-8a73-33e73e0201a9

Lokasyon Electrical & Electronics Engineering

Tarih 2014

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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