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Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients

Authors
 Eunjeong Park  ;  Hyuk-Jae Chang  ;  Hyo Suk Nam 
Citation
 JOURNAL OF MEDICAL INTERNET RESEARCH, Vol.19(4) : 120, 2017 
Journal Title
JOURNAL OF MEDICAL INTERNET RESEARCH
ISSN
 1439-4456 
Issue Date
2017
MeSH
Female ; Humans ; Machine Learning/utilization* ; Male ; Medical Informatics/methods* ; Neurologic Examination/instrumentation ; Neurologic Examination/methods* ; Stroke/diagnosis*
Keywords
machine learning ; medical informatics ; motor ; neurological examination ; stroke
Abstract
BACKGROUND: The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients. OBJECTIVE: The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing. METHODS: We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation. RESULTS: Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%. CONCLUSIONS: Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.
Files in This Item:
T201701750.pdf Download
DOI
10.2196/jmir.7092
Appears in Collections:
2. College of Dentistry (치과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Nam, Hyo Suk(남효석) ORCID logo https://orcid.org/0000-0002-4415-3995
Park, Eunjeong(박은정)
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/153556
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