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IntroductionAlzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration. MethodsThis study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions. Results and discussionThe study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis—a key behavioral pattern—with machine learning, offering a practical tool for MCI screening in both clinical and home environments.
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Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG
Abstract BACKGROUNDEarly discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. METHODSOur research is based on resting‐state electroencephalography (EEG) and the current dataset includes 137 consensus‐diagnosed, community‐dwelling Black Americans (ages 60–90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time‐varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity‐based score. RESULTSThe leave‐one‐out cross‐validation accuracy is 91.97% and 3‐fold accuracy is 91.17%. The 9 to 18 months’ progression trend prediction accuracy over an availability‐limited subset sample is 84.61%. CONCLUSIONThe EEG‐based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.
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- Award ID(s):
- 2032709
- PAR ID:
- 10442078
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Alzheimer's & Dementia
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 1552-5260
- Format(s):
- Medium: X Size: p. 145-158
- Size(s):
- p. 145-158
- Sponsoring Org:
- National Science Foundation
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