ArticleJournal of medical Internet research2017
Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Recalibrating neurological examination: from binary signs to probabilistic and multidimensional assessment using mobile technologies in acute neurology.Frontiers in neurology · 2026Article
- Noncontact Monitoring and AI-Driven Stroke Prediction: National Center for Neurological Disorders-Based Approach Using Smart Beds.Health care science · 2025Article
- Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice.Healthcare (Basel, Switzerland) · 2024Review
- Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review.Neurointervention · 2024Review
- Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network.Neurophotonics · 2023Article
- Recombination of the right cerebral cortex in patients with left side USN after stroke: fNIRS evidence from resting state.Frontiers in neurology · 2023Article
- Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine.The EPMA journal · 2022Article
- Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study.Journal of medical Internet research · 2020Article
- Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.JMIR medical informatics · 2020Article
- On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine.Pharmacogenomics and personalized medicine · 2020Review
- A Stroke Risk Detection: Improving Hybrid Feature Selection Method.Journal of medical Internet research · 2019Article
- Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods.Journal of medical Internet research · 2018Article
- Disability Adjusted Life Years due to Ischaemic Stroke Preventable by Real-Time Stroke Detection-A Cost-Utility Analysis of Hypothetical Stroke Detection Devices.Frontiers in neurology · 2018Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.
objectiveThe 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.
methodsWe 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.
resultsSignal 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%.
conclusionsSensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.