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Updated: Aug 26, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Post-stroke respiratory complications using machine learning with voice features from mobile devices.

Hae-Yeon Park1, DoGyeom Park2, Hye Seon Kang3,4

  • 1Department of Rehabilitation Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Scientific Reports
|October 6, 2022
PubMed
Summary
This summary is machine-generated.

Abnormal voice patterns detected via mobile devices can identify patients at risk of post-stroke dysphagia. Machine learning models using voice features accurately predict the need for tube feeding and respiratory complications.

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Speech-Language Pathology

Background:

  • Post-stroke dysphagia is a common complication.
  • Early identification of aspiration risk is crucial for preventing pneumonia.
  • Current diagnostic methods can be invasive or resource-intensive.

Purpose of the Study:

  • To investigate the efficacy of machine learning algorithms using mobile-recorded voice samples for classifying post-stroke dysphagia.
  • To determine if voice analysis can serve as a digital biomarker for identifying patients at risk of tube feeding and aspiration pneumonia.

Main Methods:

  • Prospective collection of voice samples from patients with swallowing disturbances using a mobile device.
  • Application of eXtreme gradient boosting multimodal models incorporating acoustic features and clinical variables.
  • Classification of patients based on need for tube feeding and risk of respiratory complications (using cough strength and chest x-ray).

Main Results:

  • Machine learning models achieved high sensitivity: 88.7% for tube feeding risk and 84.5% for respiratory complication risk.
  • Abnormal acoustic voice features were the most significant contributing factors in the predictive models.
  • A total of 449 voice samples were analyzed.

Conclusions:

  • Voice analysis via mobile devices shows promise as a non-invasive tool for assessing post-stroke dysphagia.
  • Voice features can function as effective digital biomarkers for identifying patients at risk of respiratory complications.
  • This approach may aid in early intervention and prevention of aspiration pneumonia.