Abstract:
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 s...Show MoreMetadata
Abstract:
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.
Published in: 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)
Date of Conference: 20-23 September 2023
Date Added to IEEE Xplore: 22 November 2023
ISBN Information:
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- IEEE Keywords
- Index Terms
- Mild Cognitive Impairment ,
- Early Mild Cognitive Impairment ,
- Cognitive-motor Tasks ,
- Dementia ,
- Alzheimer’s Disease ,
- Random Forest ,
- Decision Tree ,
- Machine Learning Models ,
- Cognitive Tasks ,
- K-nearest Neighbor ,
- Heart Rate Variability ,
- Machine Learning Classifiers ,
- Localization Task ,
- Gradient Boosting ,
- XGBoost ,
- Mild Cognitive Impairment Patients ,
- Heart Rate Variability Data ,
- Highest Accuracy Rate ,
- Detection Of Mild Cognitive Impairment ,
- Learning Algorithms ,
- Feature Selection Methods ,
- Heart Rate Variability Analysis ,
- Time Domain ,
- Gradient Boosting Decision Tree ,
- Frequency Domain ,
- Single-photon Emission Computed Tomography ,
- Ensemble Model ,
- 5-min Rest ,
- Wearable Devices ,
- RR Intervals
- Author Keywords
- MCI detection ,
- HRV ,
- healthy aging ,
- Neurotrack ,
- CERAD+ ,
- balance master ,
- ML
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Mild Cognitive Impairment ,
- Early Mild Cognitive Impairment ,
- Cognitive-motor Tasks ,
- Dementia ,
- Alzheimer’s Disease ,
- Random Forest ,
- Decision Tree ,
- Machine Learning Models ,
- Cognitive Tasks ,
- K-nearest Neighbor ,
- Heart Rate Variability ,
- Machine Learning Classifiers ,
- Localization Task ,
- Gradient Boosting ,
- XGBoost ,
- Mild Cognitive Impairment Patients ,
- Heart Rate Variability Data ,
- Highest Accuracy Rate ,
- Detection Of Mild Cognitive Impairment ,
- Learning Algorithms ,
- Feature Selection Methods ,
- Heart Rate Variability Analysis ,
- Time Domain ,
- Gradient Boosting Decision Tree ,
- Frequency Domain ,
- Single-photon Emission Computed Tomography ,
- Ensemble Model ,
- 5-min Rest ,
- Wearable Devices ,
- RR Intervals
- Author Keywords
- MCI detection ,
- HRV ,
- healthy aging ,
- Neurotrack ,
- CERAD+ ,
- balance master ,
- ML