A Breakthrough Study in Tremor Monitoring and Treatment

The first AI-PROGNOSIS project-related publication - "Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment",  is now available on IEEE Access.

The current research addresses one of the most common Parkinson's disease symptoms - Rest Tremor. Traditional diagnostic and treatment methods often fall short, leading to subjective assessments and limited effectiveness. An innovative AI-powered approach analyses data from Parkinson's patients, leading to a more accurate classification of treatment effectiveness and tremor severity. This is a significant step towards enhancing treatment and improving the quality of life for those living with Parkinson's.

Leveraging Higher Order Spectra and Machine Learning, the research reveals how Parkinsonian Tremor responds to different treatment strategies, paving the way to more efficient, sensitive, and comprehensible tremor monitoring.

The publication was authored by Ioannis Ziogas, Charalampos Lamprou and prof. Leontios J. Hadjileontiadis. The work was supported in part by the Khalifa University of Science and Technology and in part by the European Union’s HORIZON-RIA Program ‘‘AI-PROGNOSIS’’.

Read the full article on IEEE Xplore and learn more about the finding and their potential impact on Parkinson’s treatment and AI-powered wearable technologies: https://ieeexplore.ieee.org/document/10286508