Early Characterization of Stroke Using Video Analysis and Machine Learning

Abstract

Stroke is one of the leading causes of death and disability worldwide and requires an immediate attention as the longer the patient is left untreated, the more sever its outcomes are. Enhancing access to optimal treatment and reducing mortality rates require improving the accuracy of stroke characterization methods in prehospital settings. This study explores how video analysis and machine learning (ML) can be leveraged to identify stroke symptoms on the National Institute of Health Stroke Scale (NIHSS), with the goal of facilitating the prehospital management of patients with suspected stroke. A total of 888 videos were captured from the research group members, who mimicked stroke symptoms including facial palsy, leg and arm paresis, ataxia and dysarthria, following the criteria of the NIHSS. Multiple algorithms, utilized in earlier studies, were examined to predict these symptoms, and their performance was assessed using accuracy, sensitivity and specificity. The best method for detecting facial palsy was found using Histogram of Oriented Gradients (HOG) features in conjunction with Adaptive Boosting (AdaBoost), achieving an accuracy, sensitivity and specificity values of 97.8%, 98.0% and 97.0%, respectively. The identification of arm paresis reached 100% on all metrics using a combination of MediaPipe and SVM. For leg paresis, all algorithms had poor detection rates. The outcome for ataxia for both limbs varied. Google Cloud Speech-to-Text was used to detect dysarthria and reached 100% on all evaluation metrics. These findings suggest that video analysis and ML have the potential to assist early stroke diagnosis, but further research is needed to validate this.

Keywords: Stroke, Machine Learning, Video Analysis, NIHSS, Prehospital Diagnosis, Algorithms.

DOI: 10.54941/ahfe1004359