Early mild cognitive impairment detection using cognitive-motor tasks and machine learning

• Main Authors: Chaabene, Siwar, Bouaziz, Bassem, Boudaya, Amal, Chaari, Lotfi, Hokelmann, Anita
• Format: Conference Proceeding
• Language: English
• Subjects: Alzheimer's disease; balance master; Boosting; CERAD; Decision trees; healthy aging; Heart rate variability; HRV; Intelligent systems; MCI detection; Neurotrack; Task analysis; Technological innovation
• ISSN: 2768-7295
• DOI: 10.1109/INISTA59065.2023.10310653

Mild cognitive impairment (MCI) is a condition marked by impairment in one or more cognitive areas, but not necessarily all of them. It is frequently referred to as the stage between typical age-related cognitive decline and dementia. Recent studies had focused on different modalities to assess disorders such as dementia and Alzheimer's disease (AD). Heart rate variability (HRV) stands out among them as having the potential to identify MCI. In this paper, we propose a new MCI detection method using HRV signals. MCI patients were compared to age-matched healthy controls (HC) for the effect of performing additional cognitive and postural tasks. Twenty-four participants were enrolled to complete three tasks: a postural balance master task, two cognitive tasks called CERAD+ and Neurotrack, and baseline. HRV data were recorded during these experiments. Six machine learning (ML) models were examined for task classification including k-Nearest Neighbors, Decision tree, Random Forest, Extra Trees, Gradient Boosting, and XGBoost. To avoid over-fitting, cross-validation (CV) was employed to assess how well the built models performed. To boost accuracy, a voting ensemble classifier model is developed that combines the top ML models with the highest accuracy rates. The findings of this study demonstrated that MCI might be diagnosed with ML classifiers utilizing HRV signals, particularly when postural and cognitive functions are taken into account.