Key result
A mobile health system using the t-RIDE algorithm achieved convergence in 1.95s, yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.
Why the study?
Does a mobile health system using the t-RIDE algorithm accurately evaluate P300 amplitude for neurocognitive impairment diagnosis in subjects with and without cognitive deficits?
Cross-Sectional (n=17)
Does a mobile health system using the t-RIDE algorithm accurately evaluate P300 amplitude for neurocognitive impairment diagnosis in subjects with and without cognitive deficits?
A novel mobile health system utilizing the t-RIDE algorithm can rapidly and accurately evaluate P300 amplitude for cognitive deficit diagnosis using a single EEG channel.
Describes a sensor-cloud architecture for neurocognitive monitoring; leaves open clinical validation and adoption.
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 tradeoffs typical of other cyber-physical systems, where hardware, algorithms, and software implementations have to come together in a coherent fashion. The system 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, and n = 2 with Alzheimer's disease 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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Venuto et al. (2018) conducted a cross-sectional in Neurocognitive impairment (n=17). Mobile health system with t-RIDE algorithm for P300 detection was evaluated on Accuracy in P300 amplitude evaluation. A mobile health system using the t-RIDE algorithm achieved convergence in 1.95s, yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.
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