# Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics

arXiv: https://arxiv.org/abs/2510.15950 · HTML: https://mlsanity.com/paper/2510.15950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2009.04796/) (2020)
- [Multimodal Explainability via Latent Shift applied to COVID-19 stratification](https://mlsanity.com/paper/2212.14084/) (2022)
- [Multi-Dataset Multi-Task Learning for COVID-19 Prognosis](https://mlsanity.com/paper/2405.13771/) (2024)
- [Computer keyboard interaction as an indicator of early Parkinson's disease](https://mlsanity.com/paper/1604.08620/) (2016)
- [A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1](https://mlsanity.com/paper/2312.17506/) (2023)
- [Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2502.17503/) (2025)
- [Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection](https://mlsanity.com/paper/2410.10374/) (2024)

## Built on by (most cited first, 1 of 1)

- [Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease](https://mlsanity.com/paper/2606.25270/) (2026)

## Abstract (arXiv)

Parkinson's disease (PD) presents a growing global challenge, affecting over 10 million individuals, with prevalence expected to double by 2040. Early diagnosis remains difficult due to the late emergence of motor symptoms and limitations of traditional clinical assessments. In this study, we propose a novel pipeline that leverages keystroke dynamics as a non-invasive and scalable biomarker for remote PD screening and telemonitoring. Our methodology involves three main stages: (i) preprocessing of data from four distinct datasets, extracting four temporal signals and addressing class imbalance through the comparison of three methods; (ii) pre-training eight state-of-the-art deep-learning architectures on the two largest datasets, optimizing temporal windowing, stride, and other hyperparameters; (iii) fine-tuning on an intermediate-sized dataset and performing external validation on a fourth, independent cohort. Our results demonstrate that hybrid convolutional-recurrent and transformer-based models achieve strong external validation performance, with AUC-ROC scores exceeding 90% and F1-Score over 70%. Notably, a temporal convolutional model attains an AUC-ROC of 91.14% in external validation, outperforming existing methods that rely solely on internal validation. These findings underscore the potential of keystroke dynamics as a reliable digital biomarker for PD, offering a promising avenue for early detection and continuous monitoring.