Phenotypical Differentiation of Tremor Using Time Series Feature Extraction and Machine Learning.

ABSTRACT:

Background

The clinical diagnosis of tremor disorders depends on the interpretation of subtle movement characteristics, signs, and symptoms. Given the absence of a universally accepted biomarker, differentiation between essential tremor (ET) and tremor-dominant Parkinson's disease (PD) frequently proves to be non-trivial.

Objective

To identify generalizable tremor characteristics to differentiate ET and PD using feature extraction and machine learning (ML).

Methods

Hand accelerometer recordings from 414 patients, clinically diagnosed at six academic centers, formed an exploratory (158 ET, 172 PD) and a validation dataset (30 ET, 54 PD). Established, standardized tremor characteristics were assessed for their cross-center accuracy and validity. Supervised ML was applie

SUBMITTER: Haring V 

PROVIDER: S-EPMC12710121 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature