Sequence-based dynamic handwriting analysis for Parkinson's disease detection with one-dimensional convolutions and BiGRUs. (15th April 2021)

• Record Type: Journal Article
• Authors: Diaz, Moises; Moetesum, Momina; Siddiqi, Imran; Vessio, Gennaro
• Is Part Of: Expert systems with applications. Volume 168(2021)
• Publication Date: 2021-04-15
• Subjects: Parkinson's disease -- Dynamic handwriting analysis -- Recurrent neural networks -- Computer-aided diagnosis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114405
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Highlights: Sequence-based dynamic handwriting analysis for Parkinson's Disease identification. Combination of one-dimensional convolutions and Bi-GRU layers for classification. Robust feature learning through convolutions on features derived from handwriting. Experiments on the PaHaW and NewHandPD datasets achieve state-of-the-art results.