Walking Stability in Patients With Benign Paroxysmal Positional Vertigo: An Objective Assessment Using Wearable Accelerometers and Machine Learning

Abstract

Background: Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders leading to balance difficulties and increased fall risks. This study aims to investigate the walking stability of BPPV patients in clinical settings and propose a machine-learning-based classification method for determining the severity of gait disturbances of BPPV. Methods: Twenty-seven BPPV outpatients and twenty-seven healthy subjects completed level walking trials at self-preferred speed in clinical settings while wearing one accelerometer on the head and one on the lower trunk. Temporo-spatial variables and six walking stability related variables (root mean square (RMS), harmonic ratio (HR), gait variability, step/stride regularity, and gait symmetry) derived from the acceleration signals were analyzed. A support vector machine model (SVM) based on the gait variables of BPPV patients were developed to classify the BPPV severity of gait disturbances. Results: The results showed that BPPV patients employed a conservative gait and significantly reduced walking stability compared to the healthy controls. Significant different mediolateral HR at the lower trunk and anteroposterior step regularity at the head were found in BPPV patients among mild, moderate, and severe DHI (dizziness handicap inventory) subgroups. SVM classification achieved promising accuracies with area under the curve (AUC) = 0.87, 0.80, and 0.95 respectively for classifying the three stages of DHI subgroups. Conclusions: Results suggested that the proposed gait analysis that is based on the coupling of wearable accelerometers and machine learning provides an objective approach for assessing gait disturbances and handicapping effects of dizziness imposed by BBPV.Trial registration: The trial was registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn) on March 29, 2018. Registration number: ChiCTR1800015432 (http://www.chictr.org.cn/showproj.aspx?proj=25587).

Machine learning model based on clinical features to diagnose unexplained dizziness with patent foramen ovale

“…[16][17][18] Recent advances in artificial intelligence are transforming healthcare and there are increasing efforts to apply these breakthroughs to the diagnosis of dizziness. [19][20][21] However, there is an absence of a simple and valid screening model for the diagnosis of unexplained dizziness with PFO in clinical practice.This is the first machine learning-based model to diagnose unexplained dizziness with PFO.…”