Detecting Mild Cognitive Impairment from Spontaneous Speech by Correlation-Based Phonetic Feature Selection

Gosztolya, Gábor; Tóth, László; Grósz, Tamás; Vincze, Veronika; Hoffmann, Ildikó; Szatlóczki, Gréta; Pákáski, Magdolna; Kálmán, János

doi:10.21437/interspeech.2016-384

Cited by 35 publications

(22 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A growing body of work is highlighting the ongoing clinical validation of speech-based measures in a variety of clinical contexts. Speech has been demonstrated to have diagnostic validity for Alzheimer's disease (AD) and mild cognitive impairment (MCI) in studies using machine-learning classification models to differentiate individuals with AD/MCI from healthy individuals based on speech samples [34][35][36][37][38][39][40][41]. Additionally, speech analysis has been shown to be able to detect individuals with depression [42][43][44][45], schizophrenia [46][47][48][49], autism spectrum disorder [50], and Parkinson's disease [51,52], and can differentiate the subtypes of primary progressive aphasia and frontotemporal dementia [53][54][55].…”

Section: Clinical Validationmentioning

confidence: 99%

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Robin¹,

Harrison

Kaufman³

et al. 2020

Digit Biomark

View full text Add to dashboard Cite

Speech represents a promising novel biomarker by providing a window into brain health, as shown by its disruption in various neurological and psychiatric diseases. As with many novel digital biomarkers, however, rigorous evaluation is currently lacking and is required for these measures to be used effectively and safely. This paper outlines and provides examples from the literature of evaluation steps for speech-based digital biomarkers, based on the recent V3 framework (Goldsack et al., 2020). The V3 framework describes 3 components of evaluation for digital biomarkers: verification, analytical validation, and clinical validation. Verification includes assessing the quality of speech recordings and comparing the effects of hardware and recording conditions on the integrity of the recordings. Analytical validation includes checking the accuracy and reliability of data processing and computed measures, including understanding test-retest reliability, demographic variability, and comparing measures to reference standards. Clinical validity involves verifying the correspondence of a measure to clinical outcomes which can include diagnosis, disease progression, or response to treatment. For each of these sections, we provide recommendations for the types of evaluation necessary for speech-based biomarkers and review published examples. The examples in this paper focus on speech-based biomarkers, but they can be used as a template for digital biomarker development more generally.

show abstract

Section: Clinical Validationmentioning

confidence: 99%

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Robin¹,

Harrison

Kaufman³

et al. 2020

Digit Biomark

View full text Add to dashboard Cite

show abstract

“…A kidolgozott módszer segítségével lehetséges a páciens állapotának becslése néhány beszédfeladat elvégzése által, így könnyebben eldönthető az adott kezelés hatékonysága körülményes orvosi vizsgálatok nélkül. Hasonló módon kidolgoztunk egy automatikus módszert [2], amivel lehetővé válik az enyhe kognitív zavar detektálása csupán beszédből, ez a rendszer szűrővizsgálatok során lenne alkalmazható. Sajnos mindkét rendszer csak limitált mennyiségű adaton lett tanítva, pontosan ezért a gyakorlatban való használata jelenleg nem reális cél, ehhez lényegesen több adatra lenne szükség és megfelelő klinikai kiértékelésekre.…”

Section: Korábbi Fejlesztésekunclassified

A Mesterséges Intelligencia (AI) az egészség szolgálatában

Devosa¹,

Grósz²,

Kardos³

2020

Gradus

Self Cite

View full text Add to dashboard Cite

A Mesterséges Intelligencia lehetőségei korlátlanok, pontosabban annyira korlátoltak, amennyire az annotált anyagok mennyisége. A medikális rendszerek alkalmassá tehetők akár a beszédből történő Parkinson szűrésre, vagy a képalkotó diagnosztikai eszközökből nyert információk feldolgozásával a tüdőszűrés automatizálására. Mindehhez az itt ismertetett U-NET struktúra lehet az egyik hatékony megoldás.

show abstract

“…length of utterance and pauses) useful in identifying patients with mild cognitive impairment (MCI). In their recent work, Gosztolya et al [9] expanded the initial feature set to include descriptors (silence and filled pauses, breathing noises, laughter and coughs). Applying a number of different feature selection algorithms, they have tried to identify the most informative features for classification.…”

Section: Automatic Detection Of Dementiamentioning

confidence: 99%

An Avatar-Based System for Identifying Individuals Likely to Develop Dementia

et al. 2017

View full text Add to dashboard Cite

This paper presents work on developing an automatic dementia screening test based on patients' ability to interact and communicate -a highly cognitively demanding process where early signs of dementia can often be detected. Such a test would help general practitioners, with no specialist knowledge, make better diagnostic decisions as current tests lack specificity and sensitivity. We investigate the feasibility of basing the test on conversations between a 'talking head' (avatar) and a patient and we present a system for analysing such conversations for signs of dementia in the patient's speech and language. Previously we proposed a semi-automatic system that transcribed conversations between patients and neurologists and extracted conversation analysis style features in order to differentiate between patients with progressive neurodegenerative dementia (ND) and functional memory disorders (FMD). Determining who talks when in the conversations was performed manually. In this study, we investigate a fully automatic system including speaker diarisation, and the use of additional acoustic and lexical features. Initial results from a pilot study are presented which shows that the avatar conversations can successfully classify ND/FMD with around 91% accuracy, which is in line with previous results for conversations that were led by a neurologist.

show abstract

Detecting Mild Cognitive Impairment from Spontaneous Speech by Correlation-Based Phonetic Feature Selection

Cited by 35 publications

References 28 publications

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

A Mesterséges Intelligencia (AI) az egészség szolgálatában

An Avatar-Based System for Identifying Individuals Likely to Develop Dementia

Contact Info

Product

Resources

About