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Voice-Based Assessment of Extrapyramidal Symptoms Using Deep Learning.

Erandhi M Liyanage1, Kun-Chan Lan2, Quang Ha1

  • 1School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, NSW 2007, Australia.

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Summary
This summary is machine-generated.

This study introduces a novel voice-based AI model to assess extrapyramidal symptoms (EPS) severity. The model accurately predicts EPS using acoustic features from voice recordings, offering a new objective assessment tool.

Keywords:
DenseNetParkinsonismchromadeep learningextrapyramidal symptomsfundamental frequencyspectral contrastvoice diagnostics

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Speech Science

Background:

  • Extrapyramidal symptoms (EPS) are motor impairments affecting handwriting and speech, often associated with Parkinsonism.
  • Objective assessment of EPS severity is crucial for patient management and treatment efficacy.
  • Current methods for grading EPS rely on clinical scales administered by trained professionals.

Purpose of the Study:

  • To develop and validate an objective, voice-based tool for assessing the severity of extrapyramidal symptoms (EPS).
  • To investigate the correlation between specific acoustic features in speech and the severity of EPS.
  • To leverage transfer learning with deep neural networks for automated EPS classification from voice data.

Main Methods:

  • Voice data from 94 patients with varying EPS severity and 30 controls were collected, including recordings of vowel and consonant sounds.
  • Clinical assessment of EPS severity was performed using the Drug-Induced Extrapyramidal Side Effect Scale and Glasgow Antipsychotic Side Effect Scales.
  • A DenseNet architecture was employed for feature extraction and classification of voice data, utilizing acoustic features like MFCC, chroma, and spectral contrast.

Main Results:

  • Significant variations in Mel-frequency cepstral coefficients (MFCC), chroma, and spectral contrast were identified with increasing EPS severity.
  • A DenseNet-based model achieved 81.9% accuracy and 82.0% precision in classifying EPS severity from voice data.
  • This represents the first study to demonstrate a voice-based model for objective EPS severity assessment.

Conclusions:

  • Voice analysis, particularly specific acoustic features, can serve as an objective biomarker for extrapyramidal symptom severity.
  • The developed DenseNet model offers a promising, non-invasive tool for the early detection and monitoring of EPS.
  • This voice-based approach has the potential to enhance clinical assessment and management of conditions associated with EPS.