The Joint Effects of Acoustic and Linguistic Markers for Early Identification of Mild Cognitive Impairment

Fengyi Tang, Jun Chen, Hiroko H. Dodge, Jiayu Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI) using conversations. While the combination of linguistic and acoustic signals have been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we show that linguistic and acoustic features can be combined synergistically to identify MCI in semi-structured conversations. Using conversational data from an on-going clinical trial (Clinicaltrials.gov: NCT02871921), we find that the combination of linguistic and acoustic features on semi-structured conversations achieves a mean AUC of 82.7, significantly (p < 0.01) out-performing linguistic-only (74.9 mean AUC) or acoustic-only (65.0 mean AUC) detections on hold-out data. Additionally, features (linguistic, acoustic and combination) obtained from semi-structured conversations outperform their counterparts obtained from structured weekly conversations in identifying MCI. Some linguistic categories are significantly better at predicting MCI status (e.g., death, home) than others.

Original languageEnglish (US)
Article number702772
JournalFrontiers in Digital Health
Volume3
DOIs
StatePublished - Feb 11 2022

Keywords

  • Alzheimer's disease
  • audio and linguistic markers
  • behavioral intervention
  • conversations
  • I-CONECT project
  • mild cognitive impairment (MCI)

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine (miscellaneous)

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