Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs

TL;DR

This paper introduces a novel sequence-based classification model using 1D convolutions and BiGRUs to analyze handwriting data for Parkinson's disease detection, outperforming existing methods on benchmark datasets.

Contribution

The study presents a new deep learning model combining 1D convolutions and BiGRUs for handwriting analysis in PD detection, demonstrating superior performance over prior approaches.

Findings

Outperformed state-of-the-art on PaHaW dataset

Achieved competitive results on NewHandPD dataset

Effective use of sequential handwriting features for PD classification

Abstract

Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients' fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.