mFAST: Automatic Stoke Evaluation System for Time-Critical Treatment with Multimodal Feature Collection and Machine Learning Classification

Abstract

In this paper, we introduce an automatic evaluation system, mFAST, for the examination of neurological deficits and personalized stroke prediction. The proposed system enables mobile monitoring of stroke patients utilizing sensors and machine learning techniques. This research is composed of objective and rapid measurement of neurological deficits of stroke patients; development of stroke-triage prediction; and data collection of monitored neurological deficits and personalized stroke prediction system to rapidly recognize the symptoms and enhance the treatment in hospitals. The research proposes to implement the mobile services for FAST (Face, Arm, Speech, Time) stroke activation to measure face palsy, arm weakness, speech disturbance and to analyze the automatically collected data. The measured FAST features can be used in the prediction of stroke scores including NIHSS (National Institute of Health for Stroke Scale), CPSS (Cincinnati Pre-hospital Stroke Scale), MRC (Medial Research Council) to enable stroke patients to be treated in a restricted time frame.