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Detecting Parkinson’s Disease Using Voice Recordings From Mobile Devices

Momeni, Niloofar LU orcid ; Whitling, Susanna LU and Jakobsson, Andreas LU orcid (2024) 32nd European Signal Processing Conference, EUSIPCO 2024 p.1516-1520
Abstract

This study examines how one may detect Parkinson’s disease (PD) by analyzing voice recordings made with a mobile phone. The key objectives include creating a model that ensures fair and unbiased predictions while maintaining interpretability that is consistent with the existing literature on PD. We introduce a novel group-wise scaling method to address typical age and biological sex biases in the data sets, demonstrating an average 14% improvement as compared to conventional scaling. We also show the importance of evaluating the developed model on unseen individuals to achieve reliable classification results. The developed model offers an accuracy of 80.3% with a recall of 89.4% for unseen individuals, surpassing current... (More)

This study examines how one may detect Parkinson’s disease (PD) by analyzing voice recordings made with a mobile phone. The key objectives include creating a model that ensures fair and unbiased predictions while maintaining interpretability that is consistent with the existing literature on PD. We introduce a novel group-wise scaling method to address typical age and biological sex biases in the data sets, demonstrating an average 14% improvement as compared to conventional scaling. We also show the importance of evaluating the developed model on unseen individuals to achieve reliable classification results. The developed model offers an accuracy of 80.3% with a recall of 89.4% for unseen individuals, surpassing current state-of-the-art approaches. Furthermore, we offer insights into the decision-making of the model using SHAP values. Our analysis reveals that a low standard deviation in the slope of the fundamental frequency (indicative of monotone voice quality) increases the likelihood of classifying a PD voice. Additionally, longer unvoiced segments, increased loudness standard deviation, and jitter correlate with the presence of PD. The significance of our proposed model lies in its generalizability and reliability for early PD detection, potentially decelerating disease progression, reducing healthcare costs, and improving the quality of life for patients.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
bias, Parkinson’s disease detection, unfairness, voice features interpretability
host publication
32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
pages
5 pages
publisher
European Signal Processing Conference, EUSIPCO
conference name
32nd European Signal Processing Conference, EUSIPCO 2024
conference location
Lyon, France
conference dates
2024-08-26 - 2024-08-30
external identifiers
  • scopus:85208436867
ISBN
9789464593617
DOI
10.23919/eusipco63174.2024.10715471
language
English
LU publication?
yes
id
6802d1ce-3ebc-447b-ab19-b79bcb828c61
date added to LUP
2025-02-18 10:15:22
date last changed
2025-04-04 14:24:49
@inproceedings{6802d1ce-3ebc-447b-ab19-b79bcb828c61,
  abstract     = {{<p>This study examines how one may detect Parkinson’s disease (PD) by analyzing voice recordings made with a mobile phone. The key objectives include creating a model that ensures fair and unbiased predictions while maintaining interpretability that is consistent with the existing literature on PD. We introduce a novel group-wise scaling method to address typical age and biological sex biases in the data sets, demonstrating an average 14% improvement as compared to conventional scaling. We also show the importance of evaluating the developed model on unseen individuals to achieve reliable classification results. The developed model offers an accuracy of 80.3% with a recall of 89.4% for unseen individuals, surpassing current state-of-the-art approaches. Furthermore, we offer insights into the decision-making of the model using SHAP values. Our analysis reveals that a low standard deviation in the slope of the fundamental frequency (indicative of monotone voice quality) increases the likelihood of classifying a PD voice. Additionally, longer unvoiced segments, increased loudness standard deviation, and jitter correlate with the presence of PD. The significance of our proposed model lies in its generalizability and reliability for early PD detection, potentially decelerating disease progression, reducing healthcare costs, and improving the quality of life for patients.</p>}},
  author       = {{Momeni, Niloofar and Whitling, Susanna and Jakobsson, Andreas}},
  booktitle    = {{32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings}},
  isbn         = {{9789464593617}},
  keywords     = {{bias; Parkinson’s disease detection; unfairness; voice features interpretability}},
  language     = {{eng}},
  pages        = {{1516--1520}},
  publisher    = {{European Signal Processing Conference, EUSIPCO}},
  title        = {{Detecting Parkinson’s Disease Using Voice Recordings From Mobile Devices}},
  url          = {{http://dx.doi.org/10.23919/eusipco63174.2024.10715471}},
  doi          = {{10.23919/eusipco63174.2024.10715471}},
  year         = {{2024}},
}