scholarly journals Current State of Non-wearable Sensor Technologies for Monitoring Activity Patterns to Detect Symptoms of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Rajaram Narasimhan ◽  
Muthukumaran G. ◽  
Charles McGlade
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Activity Patterns ◽  
Wearable Sensors ◽  
Daily Activities ◽  
Wearable Sensor ◽  
Activity Data ◽  
Monitoring Activity

Mild cognitive impairment (MCI) could be a transitory stage to Alzheimer’s disease (AD) and underlines the importance of early detection of this stage. In MCI stage, though the older adults are not completely dependent on others for day-to-day tasks, mild impairments are seen in memory, attention, etc., subtly affecting their daily activities/routines. Smart sensing technologies, such as wearable and non-wearable sensors, coupled with advanced predictive modeling techniques enable daily activities/routines based early detection of MCI symptoms. Non-wearable sensors are less intrusive and can monitor activities at naturalistic environment with no interference to an individual’s daily routines. This review seeks to answer the following questions: (1) What is the evidence for use of non-wearable sensor technologies in early detection of MCI/AD utilizing daily activity data in an unobtrusive manner? (2) How are the machine learning methods being employed in analyzing activity data in this early detection approach? A systematic search was conducted in databases such as IEEE Explorer, PubMed, Science Direct, and Google Scholar for the papers published from inception till March 2019. All studies that fulfilled the following criteria were examined: a research goal of detecting/predicting MCI/AD, daily activities data to detect MCI/AD, noninvasive/non-wearable sensors for monitoring activity patterns, and machine learning techniques to create the prediction models. Out of 2165 papers retrieved, 12 papers were eligible for inclusion in this review. This review found a diverse selection of aspects such as sensors, activity domains/features, activity recognition methods, and abnormality detection methods. There is no conclusive evidence on superiority of one or more of these aspects over the others, especially on the activity feature that would be the best indicator of cognitive decline. Though all these studies demonstrate technological developments in this field, they all suggest it is far in the future it becomes an effective diagnostic tool in real-life clinical practice.

scholarly journals Applying time series analyses on continuous accelerometry data – a clinical example in older adults with and without cognitive impairment

2020 ◽  
Author(s):  
Torsten Rackoll ◽  
Konrad Neumann ◽  
Sven Passmann ◽  
Ulrike Grittner ◽  
Nadine Külzow ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Stimulation ◽  
Memory Consolidation ◽  
Activity Patterns ◽  
Accelerometer Data ◽  
Activity Data ◽  
Link Type ◽  
Early Afternoon ◽  
The Impact

AbstractIntroductionCurrent analysis approaches of accelerometry data use sum score measures which do not provide insight in activity patterns over 24 hours, and thus do not adequately depict circadian activity patterns. Here, we used a functional approach to analyze accelerometer data that models activity pattern and circadian rhythm. As a test case, we demonstrated its application in patients with mild cognitive impairment (MCI) and age-matched healthy older volunteers (HOV). Moreover, we assessed the impact of chronotype on distribution of activity data.MethodsData of two studies were pooled for this analysis. Following baseline cognitive assessment participants were provided with accelerometers for seven consecutive days. A function on scalar regression (FoSR) approach was used to analyze 24 hours accelerometer data. In a second step, analyses were controlled for chronotype using the German version of the morningness-eveningness questionnaire (d-MEQ).ResultsInformation on 47 HOV (mean age 66 SD 6 years) and 13 patients with MCI (mean age 69, SD 8 years) were available for this analysis. MCI patients displayed slightly higher activity in the morning hours as compared to HOV (maximum relative activity at 7:35 am: 75.6%, 95% CI 2.6 to 200.4%, p = 0.031). After controlling for d-MEQ, disturbed activity patterns were found in MCI of intermediate or evening chronotype, compared to HOV, i.e., MCI presented with higher activities in the morning hours (peak at 8:40 am: 357.6%, 95% CI 92.9 to 985.1, p < 0.001) and early afternoon hours (peak at 1:40 pm: 401.8%, 95% CI 63.9 to 1436.4, p < 0.001).DiscussionUsing a novel approach of FoSR, we found timeframes with higher activity levels in MCI patients compared to HOV which were not evident if sum scores of amount of activity were used. In addition, we found that previously described activity patterns as a function of chronotype swere altered in MCI patients, possibly indicating that changes in circadian rhythmicity in neurodegenerative disease are detectable using easy-to-administer accelerometry.Clinical TrialsEffects of Brain Stimulation During Nocturnal Sleep on Memory Consolidation in Patients With Mild Cognitive Impairments, https://clinicaltrials.gov/ct2/show/NCT01782391?term=NCT01782391&rank=1,ClinicalTrial.gov identifier: NCT01782391Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Patients With Mild Cognitive Impairment,https://clinicaltrials.gov/ct2/show/NCT01782365?term=NCT01782365&rank=1,ClinicalTrial.gov identifier: NCT01782365

scholarly journals Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Support Vector ◽  
Alzheimer’S Disease Dementia

Abstract Background An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia. Methods We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

scholarly journals Machine Learning Methods for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease. A Systematic Review.

2021 ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Multidimensional Data ◽  
Support Vector

Abstract Background: Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease.Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance. Conclusions: Although performance of the different models still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

scholarly journals Machine Learning methods for predicting conversion from mild cognitive impairment to Alzheimer’s disease. A systematic review.

2021 ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Multidimensional Data ◽  
Support Vector

Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease (AD) being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment (MCI), sometimes a prodromal stage of AD. We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from MCI to AD. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Most studies used MRI and PET data but also MEG. The datasets were mainly extracted from the ADNI database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimage, clinical, cognitive, biological, and behavioral) achieved the best performance. Although performance of the different models still have room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Ingrid Arevalo-Rodriguez ◽  
Nadja Smailagic ◽  
Marta Roqué-Figuls ◽  
Agustín Ciapponi ◽  
Erick Sanchez-Perez ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Mental State ◽  
Mini Mental State Examination ◽  
State Examination

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

2021 ◽  
pp. 1-15
Author(s):  
Sung Hoon Kang ◽  
Bo Kyoung Cheon ◽  
Ji-Sun Kim ◽  
Hyemin Jang ◽  
Hee Jin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apolipoprotein E ◽  
Cross Validation ◽  
Good Accuracy ◽  
External Validation ◽  
Amnestic Mild Cognitive Impairment ◽  
Temporal Region ◽  
Apolipoprotein E Genotype

Background: Amyloid (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer’s disease. However, Aβ evaluation through amyloid positron emission tomography (PET) is limited due to high cost and safety issues. Objective: We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. Methods: We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). Results: Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity. Conclusion: Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

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