COVENTRY (CU)_Alzheimer’s disease is a progressive neurodegenerative disease that often results from disruption of essential amino acids that include amyloid-beta peptides. The diagnosis of the disease usually involves an analysis of the patient’s background such as memory and judgement together with physical and radiological analysis that can sometimes be unreliable while taking longer periods and quite invasive to patients.
Scientists at the Coventry University have announced a new possible tool that can improve the diagnosis Alzheimer’s disease which affects millions of individuals across the globe and is expected to be further increase in the years ahead. The study hopes to improve patient experiences with more accurate and faster diagnosis.
Dr Fei He and Dominik Klepl, who conducted the study within the Centre for Computational Science and Mathematical Modelling at Coventry University, have produced a unique diagnosis procedure which evaluates the brain dynamics from electroencephalography (EEG) signals measuring brain electrical activity.
The discovery of the new technique uses an energy landscape concept from statistical physics to analyze the patients’ EEG signals and indicating that the results could be used to enhance detection of Alzheimer’s Disease. The new technique is also said to be more precise in diagnosing Alzheimer’s disease.
Dr Fei He, Assistant Professor, Research Centre for Computational Science and Mathematical Modelling stated that the findings indicate the significance of evaluating the global dynamics of the brain in identifying neurological disorders, The energy landscape technique and the EEG could give good tools to better the diagnose and characterize the severity of Alzheimer’s Disease.
“This work also demonstrates the importance of multi-disciplinary research, such as integrating techniques from statistical physics, signal processing and machine learning, in tackling global challenges like neurodegenerative disease” said Dr Fei He and also stated that the new technique could be used for analyzing other neurological disorders.