Machine learning-based ischemic stroke detection and categorization with non-invasive plantar pressure data

Zahra Atrachali ¹, Peyvand Ghaderyan ²

⁰Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.

Brain Research +

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June 30, 2025

View abstract on PubMed

Summary

This study introduces a machine learning algorithm using foot pressure signals for automatic stroke detection. The non-invasive method achieves 99.19% accuracy, offering a cost-effective and reliable screening tool.

Area Of Science

Biomedical Engineering
Neurology
Machine Learning

Background

Stroke diagnosis relies on expensive imaging and manual assessments, leading to delays and potential errors.
There is a critical need for accessible, cost-effective, and automated stroke detection methods.

Purpose Of The Study

To develop and validate a machine learning-based screening algorithm for stroke detection.
To utilize non-invasive foot pressure signals as a cost-effective alternative to traditional diagnostic methods.

Main Methods

A machine learning algorithm was developed using foot pressure signals recorded during walking.
Empirical Fourier Decomposition and a novel set of biomarkers were used to analyze pressure distribution.
The ReliefF algorithm, support vector machine, and K-nearest neighbors were employed for feature selection and classification.

Main Results

The algorithm was evaluated on 82 subjects (36 stroke patients, 46 controls) using 198 foot plantar sensors.
An average accuracy rate of 99.19% was achieved, demonstrating high detection performance.
The method showed robust performance against clinical factors, including stroke side and blood pressure status.

Conclusions

The proposed method offers a unique, reliable, and cost-effective automatic stroke detection solution.
It successfully identifies stroke using a minimal set of biomarkers from toe and finger regions.
This approach has the potential to significantly improve early stroke diagnosis and patient management.

Keywords:

Clinical diagnosis model Foot pressure pattern recognition Gait dysfunction Plantar regional analysis