Assessing Cerebellar Disorders With Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches

TL;DR

This study develops a method combining time-frequency analysis and autoregressive hidden Markov models to extract behavioral biomarkers from wearable sensor data, effectively distinguishing cerebellar ataxia patients from controls and estimating disease severity.

Contribution

It introduces a novel approach that uses wearable inertial sensors with advanced modeling techniques for accurate diagnosis and severity assessment of cerebellar ataxias.

Findings

High accuracy in differentiating ataxia patients from healthy controls

Effective separation of ataxia from Parkinson's disease

Reliable estimation of disease severity from short data segments

Abstract

We use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors while participants perform clinical assessment tasks, and with them estimate disease status and severity. A short period of data collection ($<$ 5 minutes) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson's disease, and to give estimates of disease severity.