Classification of Alzheimer’s Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation

2020 ◽  
Vol 75 (4) ◽  
pp. 1253-1261 ◽  
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Francesca Alù ◽  
Matteo Menna ◽  
Elda Judica ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Physiological Aging

Classification of Alzheimer’s disease based on brain MRI and machine learning

2019 ◽  
Vol 32 (7) ◽  
pp. 1927-1936 ◽  
Author(s):  
Zhao Fan ◽  
Fanyu Xu ◽  
Xuedan Qi ◽  
Cai Li ◽  
Lili Yao
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Mri

scholarly journals Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease

2019 ◽  
Vol 23 ◽  
pp. 101811 ◽  
Author(s):  
Jun Pyo Kim ◽  
Jeonghun Kim ◽  
Yu Hyun Park ◽  
Seong Beom Park ◽  
Jin San Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Hierarchical Classification

[P4-007]: MACHINE LEARNING IN AUTOMATED CLASSIFICATION OF ADVERSE EVENTS IN CLINICAL STUDIES OF ALZHEIMER's DISEASE

2017 ◽  
Vol 13 (7S_Part_26) ◽  
pp. P1256-P1256 ◽  
Author(s):  
Pradeep Anand Ravindranath ◽  
Stefania Bruschi ◽  
Karin Ernstrom ◽  
Rema Raman ◽  
Tiffany Chow ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Adverse Events ◽  
Clinical Studies ◽  
Automated Classification

scholarly journals Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 778
Author(s):  
Nitsa J. Herzog ◽  
George D. Magoulas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Progressive Dementia ◽  
Machine Learning Classification

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

Sign in / Sign up

Export Citation Format

Share Document