There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Guanpeng Li |
| Institution: | Southern University of Science and Technology |
| Department: | School of Public Health and Emergency Management |
| Country: | |
| Proposed Analysis: | Parkinson’s disease (PD) and Alzheimer’s disease dementia (AD) are the two most common neurodegenerative diseases. Around 50 million people have the symptom of Dementia (DM), and 10 million people have PD worldwide currently, with over 10 million new cases every year. As professionals still research and develop new drugs aiming to modify illness progression or alleviate symptoms, early diagnosis of neurodegenerative diseases is crucial, although highly challenging. Novel robust biomarkers are urgently required quantitatively to assess their efficacy in differential and early diagnosis. This paper proposed a supervised classification framework that used body-wear accelerometer signals from a free-living environment to classify PD, DM, and Healthy Control (HC) participants, mainly focusing on categorizing DM from PD to follow the disease progression. The framework consists of feature extraction, feature selection, classification modeling, and quantitative evaluation. The results show that the Logistic Regression model can achieve up to 83% accuracy in patients classification, which performs best with explicit meaning. However, we cannot overlook the limitations of the early stage of the study. The following research will seriously treat the clinical scales and age-matched and size-matched control groups. |
| Additional Investigators |
