CERIAH, Institut de L'Audition, Institut Pasteur, Paris, France.
Department of Statistics & Applied Probability, University of California, Santa Barbara (UCSB), Santa Barbara, California, United States of America.
PLoS One. 2023 Apr 26;18(4):e0284667. doi: 10.1371/journal.pone.0284667. eCollection 2023.
Medical diagnostic methods that utilise modalities of patient symptoms such as speech are increasingly being used for initial diagnostic purposes and monitoring disease state progression. Speech disorders are particularly prevalent in neurological degenerative diseases such as Parkinson's disease, the focus of the study undertaken in this work. We will demonstrate state-of-the-art statistical time-series methods that combine elements of statistical time series modelling and signal processing with modern machine learning methods based on Gaussian process models to develop methods to accurately detect a core symptom of speech disorder in individuals who have Parkinson's disease. We will show that the proposed methods out-perform standard best practices of speech diagnostics in detecting ataxic speech disorders, and we will focus the study, particularly on a detailed analysis of a well regarded Parkinson's data speech study publicly available making all our results reproducible. The methodology developed is based on a specialised technique not widely adopted in medical statistics that found great success in other domains such as signal processing, seismology, speech analysis and ecology. In this work, we will present this method from a statistical perspective and generalise it to a stochastic model, which will be used to design a test for speech disorders when applied to speech time series signals. As such, this work is making contributions both of a practical and statistical methodological nature.
利用患者症状（如言语）模态的医学诊断方法越来越多地被用于初始诊断目的和监测疾病状态进展。言语障碍在神经退行性疾病中尤为普遍，如帕金森病，本研究的重点。我们将展示最先进的统计时间序列方法，这些方法将统计时间序列建模和信号处理的元素与基于高斯过程模型的现代机器学习方法结合起来，开发出方法来准确检测患有帕金森病的个体的言语障碍的核心症状。我们将表明，所提出的方法在检测共济失调性言语障碍方面优于言语诊断的标准最佳实践，我们将特别关注一项广受欢迎的帕金森病数据言语研究的详细分析，该研究公开了所有可重复的结果。所开发的方法基于一种专门的技术，该技术在医学统计学中并未广泛采用，但在信号处理、地震学、言语分析和生态学等领域取得了巨大成功。在这项工作中，我们将从统计角度介绍这种方法，并将其推广到随机模型中，该模型将用于在应用于言语时间序列信号时设计言语障碍测试。因此，这项工作在实践和统计方法学方面都做出了贡献。
