公开(公告)号：US20240257971A1

公开(公告)日：2024-08-01

申请号：US18430445

申请日：2024-02-01

Applicant: Arizona Board of Regents on behalf of the University of Arizona

Inventor： William Killgore ,  Janet Roveda ,  Jerzy Rozenblit ,  Ao Li ,  Huayu Li

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: A virtual reality neuropsychological assessment (VRNA) system uses a deep learning network and a VR headset to administer multi-domain assessments of human cognitive performance. The deep learning network is trained to identify features in sensor data indicative of neuropsychological performance and classify users based on the features identified in the sensor data. The VR headset provides a user with a virtual simulation of an activity involving decision-making scenarios. During the virtual simulation, sensor data via a plurality of sensors of the VR headset is captured. The sensor data is applied to the deep learning network to identify features of the user and classify the user based on the features into a neuropsychological domains, such as attention, memory, processing speed, and executive function. Sensor data includes eye-tracking, hand-eye motor coordination, reaction time, working memory, learning and delayed memory, and inhibitory control.

公开(公告)号：US20190076098A1

公开(公告)日：2019-03-14

申请号：US16125177

申请日：2018-09-07

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF THE UNIVERSITY OF ARIZONA

Inventor： Ao Li ,  Stuart F Quan ,  Janet Meiling Roveda

IPC: A61B5/00 ,  A61B5/1455 ,  G16H50/30 ,  G06N3/08 ,  A61B5/18 ,  A61B5/107 ,  A61B5/0205

Abstract: The present disclosure provides systems and methods for determining the presence and severity of sleep disordered breathing in a patient based on the output of a low-cost at-home diagnostic and the results of a health questionnaire. The low-cost at-home diagnostic is a simple photoplethysmographic survey to detect oxygen saturation overnight. Minimum oxygen saturation and other metrics are determined from the photoplethysmographic survey and applied, in combination with the health questionnaire data, to a set of artificial neural networks. Each artificial neural network corresponds to a respective degree of severity of sleep disordered breathing, according to rate of occurrence of apnea and hypopnea events during sleep. Each artificial neural network is trained with a respective subset of clinical data generated from a large population of individuals, to reduce both the false positive and false negative rate of the classifier.