InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait

TL;DR

InceptoFormer is a neural framework combining multi-scale convolutional features and transformer models to accurately evaluate Parkinson's Disease severity from gait signals, outperforming existing methods.

Contribution

It introduces a novel multi-signal neural architecture with Inception1D and transformer components for PD severity assessment from gait data.

Findings

Achieves 96.6% accuracy in PD severity classification

Outperforms existing state-of-the-art methods

Effectively handles class imbalance with oversampling strategy

Abstract

We present InceptoFormer, a multi-signal neural framework designed for Parkinson's Disease (PD) severity evaluation via gait dynamics analysis. Our architecture introduces a 1D adaptation of the Inception model, which we refer to as Inception1D, along with a Transformer-based framework to stage PD severity according to the Hoehn and Yahr (H&Y) scale. The Inception1D component captures multi-scale temporal features by employing parallel 1D convolutional filters with varying kernel sizes, thereby extracting features across multiple temporal scales. The transformer component efficiently models long-range dependencies within gait sequences, providing a comprehensive understanding of both local and global patterns. To address the issue of class imbalance in PD severity staging, we propose a data structuring and preprocessing strategy based on oversampling to enhance the representation of underrepresented severity levels. The overall design enables to capture fine-grained temporal variations and global dynamics in gait signal, significantly improving classification performance for PD severity evaluation. Through extensive experimentation, InceptoFormer achieves an accuracy of 96.6%, outperforming existing state-of-the-art methods in PD severity assessment. The source code for our implementation is publicly available at https://github.com/SafwenNaimi/InceptoFormer