A Mobile Health System for Neurocognitive Impairment Evaluation based on P300 Detection

  • Authors:
    D, De Venuto (Politecnico di Bari), V.F. Annese (Politecnico di Bari), G. Mezzina (Politecnico di Bari), F. Scioscia (Politecnico di Bari), M. Ruta (Politecnico di Bari), E. Di Sciascio (Politecnico di Bari), Alberto Sangiovanni-Vincentelli (UC/Berkeley)
    Publication ID:
    P091056
    Publication Type:
    Paper
    Received Date:
    1-Jun-2017
    Last Edit Date:
    5-Jun-2017
    Research:
    2386.004 (University of California/Berkeley)

Abstract

A new mobile healthcare system for neuro-cognitive function monitoring and treatment is presented. The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering and in-cloud distribution to specialized medical personnel. As such it presents trade-offs typical of other CyberPhysical System, where hardware, algorithms and software implementations have to come together in a coherent fashion. The systems is based on spatio-temporal detection and characterization of a specific brain potential, called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects (n=12 healthy, n=3 Mildly Cognitive Impaired (MCI) and n=2 with Alzheimer Disease (AD) involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s) yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.

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